# embeddings_queue

created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36e8-934e-11ee-8575-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 0, "chroma:document": "average memory usage and F1-Score, IWTM outperforms simple Multi-Layered Arti\ufb01cial Neural Networks,\nDecision Trees, Support Vector Machines, K-Nearest Neighbor, Random Forest, Gradient Boosted Trees\n(XGBoost), Explainable Boosting Machines (EBMs), as well as the standard and real-value weighted TMs.\nIWTM \ufb01nally outperforms Neural Additive Models on Fraud Detection and StructureBoost on CA-58 in\nterms of Area Under Curve, while performing competitively on COMPAS."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36e9-934e-11ee-b24d-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 0, "chroma:document": "INDEX TERMS Tsetlin machine, integer-weighted Tsetlin machine, interpretable AI, interpretable machine\nlearning, XAI, rule-based learning, decision support system.\nI. INTRODUCTION\nInterpretable Machine Learning refers to machine learning\nmodels that obtain transparency by providing the reasons\nbehind their output. Linear Regression, Logistic Regression,\nDecision Trees, and Decision Rules are traditional inter-\npretable machine learning approaches. However, as discussed"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36ea-934e-11ee-a1c3-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 0, "chroma:document": "in [1], the degree of interpretability of these algorithms vary.\nMore importantly, such methods struggle with obtaining high\nThe associate editor coordinating the review of this manuscript and\napproving it for publication was Pengcheng Liu\n .accuracy for complex problems, especially in comparison to\ndeep learning. On the other hand, deep learning inference\ncannot easily be interpreted [2] and is thus less suitable for\nhigh-stakes domains such as credit-scoring [3], [4], medicine"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36eb-934e-11ee-af6b-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 0, "chroma:document": "[5], [6], bioinformatics [7], [8], churn prediction [9], [10]\nhealthcare, and criminal justice [11]. Therefore, developing\nmachine learning algorithms capable of achieving a better\ntrade-off between interpretability and accuracy is of signi\ufb01-\ncant importance and continues to be an active area of research.\nOne of the ways to tackle the above-stated research prob-\nlem is explaining the deep learning inference. Different"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36ec-934e-11ee-be25-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 0, "chroma:document": "VOLUME 9, 2021This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/8233"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36ed-934e-11ee-868c-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 1, "chroma:document": "K. D. Abeyrathna et al.: Extending the TM With Integer-Weighted Clauses for Increased Interpretability\napproaches have been proposed for local interpretability, i.e.,\nexplaining individual predictions [12]. However, they fail to\nprovide clear explanations of model behavior globally [13].\nRecently, Agarwal et al. [11] proposed a novel deep learning\napproach that belongs to the family of Neural Additive Mod-\nels (NAMs). Even though NAMs are inherently interpretable,"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36ee-934e-11ee-8da4-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 1, "chroma:document": "they are still surpassed by regular deep learning algorithms\nwhen it comes to accuracy [11].\nInterpretable linear and rule-based methods can sometimes\noffer a better trade-off between interpretability and accuracy.\nLearning propositional formulae to represent data patterns\nhas a long history, with association rule learning [14] being\none well-known approach, which has been used to predict\nsequential events [15]. Other examples include the work of"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36ef-934e-11ee-8e10-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 1, "chroma:document": "Feldman on the hardness of learning formulae in Disjunctive\nNormal Form (DNF) [16] and Probably Approximately Cor-\nrect (PAC) learning, which has provided fundamental insight\ninto machine learning as well as a framework for learning for-\nmulae in DNF [17]. Approximate Bayesian approaches have\nrecently been introduced to provide more robust learning of\nrules [18], [19]. Furthermore, hybrid Logistic Circuits have\nhad success in image classi\ufb01cation [20]. Logical operators in"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36f0-934e-11ee-8143-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 1, "chroma:document": "one layer of the logistic circuit are wired to logical operators\nin the next layer, and the whole system can be represented\nas a logistic regression function. This approach uses local\nsearch to build a Bayesian model that captures the logical\nexpression, and learns to classify by employing stochastic\ngradient descent. Yet, in general, rule-based machine learning\nscales poorly and is prone to noise. Indeed, for data-rich\nproblems, in particular those involving natural language and"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36f1-934e-11ee-86ab-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 1, "chroma:document": "sensory inputs, rule-based machine learning is inferior to\ndeep learning.\nTsetlin Machines (TMs) are entirely based on logical oper-\nators and summation, founded on TA-based bandit learning\n[21]\u2013[27]. Despite being rule-based, TMs have obtained com-\npetitive performance in terms of accuracy, memory footprint,\nand inference speed on diverse benchmarks, including image\nclassi\ufb01cation, regression, natural language understanding,\nand game-playing. Employing a team of TA [28], a TM learns"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36f2-934e-11ee-91ac-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 1, "chroma:document": "a linear combination of conjunctive clauses in propositional\nlogic, producing decision rules similar to the branches in\na decision tree (e.g., ifXsatis\ufb01es condition A and not\ncondition B then Y=1) [23].\nA. RECENT PROGRESS ON TMS\nRecent research on TMs reports several distinct TM prop-\nerties. The TM performs competitively on several classic\ndatasets, such as Iris, Digits, Noisy XOR, and MNIST,\ncompared to Support Vector Machines (SVMs), Decision"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36f3-934e-11ee-9736-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 1, "chroma:document": "Trees (DTs), Random Forest (RF), Naive Bayes Classi\ufb01er,\nLogistic Regression, and simple Arti\ufb01cial Neural Networks\n(ANNs) [21]. The TM can further be used in convolu-\ntion, providing competitive performance on MNIST, Fashion-\nMNIST, and Kuzushiji-MNIST, in comparison with CNNs,\nK-Nearest Neighbour (KNN), SVMs, RF, Gradient Boost-\ning, BinaryConnect, Logistic Circuits and ResNet [29]. TheTM has also achieved promising results in text classi\ufb01-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36f4-934e-11ee-9cb5-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 1, "chroma:document": "cation by using the conjunctive clauses to capture textual\npatterns [23]. Further, hyper-parameter search can be sim-\npli\ufb01ed with multi-granular clauses, eliminating the pattern\nspeci\ufb01city parameter [25]. By indexing the clauses on the\nfeatures that falsify them, up to an order of magnitude faster\ninference and learning has been reported [26]. Furthermore,\nTM hardware has demonstrated up to three orders of magni-\ntude reduced energy usage and faster learning, compared to"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36f5-934e-11ee-a9a8-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 1, "chroma:document": "neural networks alike [27]. While TMs are binary through-\nout, binarization schemes open up for continuous input [30].\nFinally, the Regression Tsetlin Machine addresses continuous\noutput problems, obtaining on par or better accuracy on pre-\ndicting dengue incidences, stock price, real estate value and\naerofoil noise, in comparison to Regression Trees, RF, and\nSupport Vector Regression [24].\nB. PAPER CONTRIBUTIONS\nAlthough TMs are capable of achieving competitive perfor-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36f6-934e-11ee-bcc7-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 1, "chroma:document": "mance levels, they often require a large number of clauses\nto do so, which impedes interpretability. To overcome this\naccuracy-interpretability challenge in TMs, we propose the\nIWTM, encompassing the following contributions.\n\u2022We extend each clause with the Stochastic Searching\non the Line (SSL) automaton [31]. This automaton is\nto learn an effective weight for its clause by interacting\nwith the corresponding TA team. As a result, the set\nof clauses can be rendered signi\ufb01cantly more compact,"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36f7-934e-11ee-b6ca-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 1, "chroma:document": "without sacri\ufb01cing accuracy.\n\u2022Through the above scheme, we allow the TM to identify\nwhich clauses are inaccurate. These clauses are given\nsmaller weights so that they must team up to obtain high\naccuracy as a team. Furthermore, the clauses that are\nsuf\ufb01ciently accurate are assigned larger weights so that\nthey can operate more independently.\n\u2022Empirically, we evaluate the IWTM using the eight data\nsets: Bankruptcy, Balance Scale, Breast Cancer, Liver"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36f8-934e-11ee-867b-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 1, "chroma:document": "Disorders, Heart Disease, Fraud Detection, COMPAS,\nand CA-58. The results show that IWTM on average\nuses 6.5 times fewer literals than the vanilla TM, and\n120.0 times fewer literals than a TM with real-valued\nweights [22]. Furthermore, performance is competitive\nwith recent state-of-the-art machine learning models.\nC. PAPER ORGANIZATION\nIn Section II, we present the IWTM and describe how each\nteam of TA, composing the clauses, is extended with the"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36f9-934e-11ee-8854-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 1, "chroma:document": "SSL. We further discuss the adaptive learning procedure\nwhich simultaneously update both clauses and weights. Then\nin Section III, we evaluate the classi\ufb01cation accuracy of\nIWTM empirically using \ufb01ve datasets, including a study\nof rule extraction for Bankruptcy prediction in detail and\ncompare against several ANNs, DTs, SVMs, KNN, RF, Gra-\ndient Boosted Trees (XGBoost), EBMs (the current state-of-\nthe-art of Generalized Additive Models (GAMs) [32], [33])\n8234 VOLUME 9, 2021"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36fa-934e-11ee-ace8-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 2, "chroma:document": "K. D. Abeyrathna et al.: Extending the TM With Integer-Weighted Clauses for Increased Interpretability\nFIGURE 1. Transition graph of a two-action Tsetlin Automaton.\nand competing TMs. Further, we contrast the performance\nof IWTM against reported results on recent state-of-the-art\nmachine learning models, namely NAMs [11] and Structure-\nBoost [34]. Finally, the paper is concluded in Section IV.\nII. INTEGER-WEIGHTED TSETLIN MACHINE\nIn this section, we introduce the integer weighting scheme"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36fb-934e-11ee-a33a-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 2, "chroma:document": "for TMs. First, we cover the basics of TA, which determine\nthe composition of the TM clauses. Then we introduce the\nbasic TM structure, before we present how integer weights\nare assigned to the TM clauses. We cover how the individual\nclauses are trained to learn sub-patterns and how the weight\nvalues are updated using SSL. We conclude the section by\nanalysing the computational complexity of the IWTM.\nA. TSETLIN AUTOMATA\nIn the TM, a collective of two-action TA [28] (reviewed in"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36fc-934e-11ee-ba35-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 2, "chroma:document": "[35]) is used for bandit-based learning. FIGURE 1 shows a\ntwo-action TA with 2N states. As illustrated, a TA decides its\nnext action from its present state. States from 1 to Ntrigger\nAction 1, while states from N+1 to 2N trigger Action 2.\nThe TA iteratively interacts with an environment. At each\niteration, the environment produces a reward or a penalty in\nresponse to the action performed by the TA, according to\nan unknown probability distribution. Reward feedback rein-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36fd-934e-11ee-b793-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 2, "chroma:document": "forces the action performed and penalty feedback weakens it.\nIn order to reinforce an action, the TA changes state towards\none of the \u2018\u2018deeper\u2019\u2019 states, direction depending on the current\nstate. Conversely, an action is weakened by changing state\ntowards the center states (N /N+1). Hence, penalty feedback\neventually forces the TA to change action, shifting its state\nfrom NtoN+1 or vice versa. In this manner, with a suf\ufb01cient\nnumber of states, a TA converges to perform the action with"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36fe-934e-11ee-bf2b-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 2, "chroma:document": "the highest probability of receiving a reward \u2013 the optimal\naction \u2013 with probability arbitrarily close to unity, as long as\nthe reward probability is greater than 0.5 [28].\nB. TM STRUCTURE\nThe goal of a basic TM is to categorize input feature vec-\ntorsXinto one of two classes, y\u2208 {0, 1}. As shown in\nFIGURE 2,Xconsists of opropositional variables, xk\u2208\n{0,1}o. Further, a TM also incorporates the negation \u00acxkof"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e36ff-934e-11ee-8059-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 2, "chroma:document": "the variables to capture more sophisticated patterns. Togetherthese are referred to as literals: L=[x1,x2,..., xo,\n\u00acx1,\u00acx2,...,\u00acxo]=[l1,l2,..., l2o].\n1) CLAUSE CONSTRUCTION\nAt the core of a TM one \ufb01nds a set of mconjunctive clauses.\nThe conjunctive clauses are to capture the sub-patterns asso-\nciated with each output y. All of the clauses in the TM\nreceive identical inputs, which is the vector of literals L.\nWe formulate a TM clause as follows:\ncj=\u22c0\nk\u2208Ijlk. (1)"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3700-934e-11ee-a77d-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 2, "chroma:document": "cj=\u22c0\nk\u2208Ijlk. (1)\nNotice that each clause, indexed by j, includes distinct\nliterals. The indexes of the included literals are contained in\nthe set Ij\u2286{1,..., 2o}. For the special case of Ij=\u2205, i.e.,\nan empty clause, we have:\ncj={\n1during learning\n0otherwise.(2)\nThat is, during learning, empty clauses output 1 and during\nclassi\ufb01cation they output 0.\nIt is the two-action TAs that assign literals to clauses. Each\nclause is equipped with 2 \u00d7oTAs, one per literal k, as shown"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3701-934e-11ee-b272-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 2, "chroma:document": "inClause-1 of FIGURE 2. The TA states from 1 to Nmap\nto the exclude action, which means that the corresponding\nliteral is excluded from the clause. For states from N+1 to\n2N, the decision becomes include, i.e., the literal is included\ninstead. The states of all the TAs in all of the clauses are\njointly stored in the matrix A:A=(aj,k)\u2208{1,..., 2N}m\u00d72o,\nwith jreferring to the clause and kto the literal. Hence, the\nliteral indexes contained in the set Ijcan be expressed as\nIj={k|aj,k>N,1\u2264k\u22642o}."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3702-934e-11ee-91f4-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 2, "chroma:document": "Ij={k|aj,k>N,1\u2264k\u22642o}.\n2) CLAUSE OUTPUT\nThe output of the clauses can be produced as soon as the deci-\nsions of the TAs are known. Since the clauses are conjunctive,\nthey evaluate to 0 if any of the literals included are of value 0.\nFor a given input X, let the set I1\nXcontain the indexes of the\nliterals of value 1. Then the output of clause jcan be expressed\nas:\ncj={\n1 if Ij\u2286I1\nX,\n0 otherwise.(3)\nIn the following, we let the vector Cdenote the complete"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3703-934e-11ee-86be-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 2, "chroma:document": "set of clause outputs C=(cj)\u2208{0, 1}m, as de\ufb01ned above.\n3) CLASSIFICATION IN TM\nThe TM classi\ufb01es data into two classes, which means that\nsub-patterns associated with both classes must be identi\ufb01ed.\nThis is done by dividing clauses into two groups. Clauses with\nodd index are assigned positive polarity (c+\nj), and they are\nto capture sub-patterns of output y=1. Clauses with even\nindex, on the other hand, are assigned negative polarity (c\u2212\nj)\nand they seek the sub-patterns of output y=0."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3704-934e-11ee-a38b-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 2, "chroma:document": "j)\nand they seek the sub-patterns of output y=0.\nVOLUME 9, 2021 8235"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3705-934e-11ee-97e3-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 3, "chroma:document": "K. D. Abeyrathna et al.: Extending the TM With Integer-Weighted Clauses for Increased Interpretability\nFIGURE 2. The Integer Weighted Tsetlin Machine structure.\nOnce a clause recognizes a sub-pattern, it outputs 1, casting\na vote according to its polarity. The \ufb01nal output of the TM\nis found by summing up the clause outputs, subtracting the\nvotes of the clauses with negative polarity from the votes of\nthe clauses with positive polarity. With vbeing the difference\nin clause output, v=\u2211\njc+\nj\u2212\u2211\njc\u2212"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3706-934e-11ee-93c3-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 3, "chroma:document": "in clause output, v=\u2211\njc+\nj\u2212\u2211\njc\u2212\nj, the output of the TM\nis decided as follows:\n\u02c6y={\n1 if v\u22650\n0 if v<0.(4)\nC. INCORPORATING INTEGER WEIGHTS INTO THE TM\nIn contrast to the weighting scheme proposed by Phoulady et\nal.[22], which employs real-valued weights that require mul-\ntiplication and an additional hyperparameter, our scheme is\nparameter-free and uses increment and decrement operations\nto update the weights.\n1) CLASSIFICATION IN IWTM"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3707-934e-11ee-a44d-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 3, "chroma:document": "to update the weights.\n1) CLASSIFICATION IN IWTM\nThe weights decide the impact of each clause during classi\ufb01-\ncation, replacing Eq. 4 with:\n\u02c6y={\n1 if\u2211\njw+\njc+\nj\u2212\u2211\njw\u2212\njc\u2212\nj\u22650\n0 otherwise.(5)Above, w+\njis the weight of the jthclause with positive polar-\nity, while w\u2212\njis the weight of the jthclause with negative\npolarity.\nD. LEARNING PROCEDURE\nIn this sub-section, we \ufb01rst discuss how individual clauses are\ntrained to learn sub-patterns. Then the procedure of updating"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3708-934e-11ee-a937-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 3, "chroma:document": "weights is explained in detail.\n1) CLAUSE LEARNING\nA TM learns online, processing one training example (X ,y)\nat a time. Within each clause, a local team of TAs decide the\nclause output by selecting which literals are included in the\nclause. Jointly, the TA teams thus decide the overall output\nof the TM, mediated through the clauses. This hierarchical\nstructure is used to update the state of each TA, with the\npurpose of maximizing output accuracy. We achieve this with"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3709-934e-11ee-bb48-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 3, "chroma:document": "two kinds of reinforcement: Type I and Type II feedback.\nType I and Type II feedback control how the individual\nTAs either receive a reward, a penalty, or inaction feedback,\ndepending on the context of their actions. In the following\nwe focus on clauses with positive polarity. For clauses with\nnegative polarity, Type I feedback replaces Type II, and vice\nversa.\n8236 VOLUME 9, 2021"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e370a-934e-11ee-a4e2-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 4, "chroma:document": "K. D. Abeyrathna et al.: Extending the TM With Integer-Weighted Clauses for Increased Interpretability\nType I feedback: Type I feedback consists of two sub-\nfeedback schemes: Type Ia and Type Ib. Type Ia feedback\nreinforces include actions of TAs whose corresponding literal\nvalue is 1, however, only when the clause output also is 1.\nType Ib feedback combats over-\ufb01tting by reinforcing exclude\nactions of TAs when the corresponding literal is 0 or when"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e370b-934e-11ee-838c-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 4, "chroma:document": "the clause output is 0. Consequently, both Type Ia and Type\nIb feedback gradually force clauses to output 1.\nType I feedback is given to clauses with positive polarity\nwhen y=1. This stimulates suppression of false negative\noutput. To diversify the clauses, they are targeted for Type I\nfeedback stochastically as follows:\np+\nj=\uf8f1\n\uf8f2\n\uf8f31 with probabilityT\u2212max(\u2212T,min(T,v))\n2T,\n0 otherwise.\n(6)\nHere, p+\njis the decision whether to target clause jwith posi-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e370c-934e-11ee-949a-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 4, "chroma:document": "tive polarity for feedback. The user set target Tfor the clause\noutput sum vdecides how many clauses should be involved\nin learning a particular sub-pattern. Higher Tincreases the\nrobustness of learning by allocating more clauses to learn\neach sub-pattern. The decisions for the complete set of posi-\ntive clauses are organized in the vector P+=(p+\nj)\u2208{0, 1}m\n2.\nSimilarly, decisions for the complete set of negative clauses\ncan be found in P\u2212=(p\u2212\nj)\u2208{0, 1}m\n2."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e370d-934e-11ee-ba0c-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 4, "chroma:document": "can be found in P\u2212=(p\u2212\nj)\u2208{0, 1}m\n2.\nIf a clause is eligible to receive feedback per Eq. 6, the\nindividual TAs of the clause are singled out stochastically\nusing a user-set parameter s(s\u22651). The decision whether the\nkthTA of the jthclause of positive polarity is to receive Type\nIa feedback, r+\nj,k, and Type Ib feedback, q+\nj,k, are stochastically\nmade as follows:\nr+\nj,k=\uf8f1\n\uf8f2\n\uf8f31 with probabilitys\u22121\ns,\n0 otherwise.(7)\nq+\nj,k=\uf8f1\n\uf8f2\n\uf8f31 with probability1\ns,\n0 otherwise.(8)"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e370e-934e-11ee-be77-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 4, "chroma:document": "j,k=\uf8f1\n\uf8f2\n\uf8f31 with probability1\ns,\n0 otherwise.(8)\nThe above decisions are respectively stored in the two\nmatrices R+andQ+, i.e., R+=(r+\nj,k)\u2208 {0, 1}m\u00d72o\nandQ+=(q+\nj,k)\u2208 {0, 1}m\u00d72o. Using the complete set\nof conditions, TA indexes selected for Type Ia are IIa=\n{(j,k)|lk=1\u2227c+\nj=1\u2227p+\nj=1\u2227r+\nj,k=\n1}.Similarly TA indexes selected for Type Ib are IIb= {\n(j,k)|(lk=0\u2228c+\nj=0)\u2227p+\nj,y=1\u2227q+\nj,k=1}\n.\nOnce the indexes of the TAs are identi\ufb01ed, the states of"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e370f-934e-11ee-b898-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 4, "chroma:document": "those TAs are updated. Available updating options are \u2295and\n\u2296, where\u2295adds 1 and\u2296subtracts 1 from the current state.\nThe processing of the training example ends with the state\nmatrix A+being updated as follows: A+\u2190(\nA+\u2295IIa)\n\u2296IIb.\nType II feedback: Type II feedback is given to clauses\nwith positive polarity for target output y=0. Clauses to\nreceive Type II feedback are again selected stochastically.\nThe decision for the jthclause of positive polarity is madeas follows:\np+\nj=\uf8f1\n\uf8f2"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3710-934e-11ee-86d4-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 4, "chroma:document": "p+\nj=\uf8f1\n\uf8f2\n\uf8f31 with probabilityT+max(\u2212T,min(T,v))\n2T,\n0 otherwise.\n(9)\nThe idea behind Type II feedback is to change the out-\nput of the affected clauses from 1 to 0. This is achieved\nsimply by including a literal of value 0 in the clause. TAs\nselected for Type II can accordingly be found in the index\nset:III={(j, k)|lk=0\u2227c+\nj=1\u2227p+\nj=1}.To obtain\nthe intended effect, these TAs are reinforced to include their\nliterals in the clause by increasing their corresponding states:\nA+\u2190A+\u2295III."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3711-934e-11ee-83d4-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 4, "chroma:document": "A+\u2190A+\u2295III.\nWhen training has been completed, the \ufb01nal decisions\nof the TAs are recorded, and the resulting clauses can be\ndeployed for operation.\n2) WEIGHT LEARNING\nThe learning of weights is based on increasing the weight of\nclauses that receive Type Ia feedback (due to true positive\noutput) and decreasing the weight of clauses that receive\nType II feedback (due to false positive output). The overall\nrationale is to determine which clauses are inaccurate and"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3712-934e-11ee-b94f-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 4, "chroma:document": "thus must team up to obtain high accuracy as a team (low\nweight clauses), and which clauses are suf\ufb01ciently accurate\nto operate more independently (high weight clauses).\nThe weight updating procedure is summarized in Algo-\nrithm 1and in the \ufb02owchart in FIGURE 3. Here, wj(n) is the\nweight of clause jat the nthtraining round (ignoring polarity\nto simplify notation). The \ufb01rst step of a training round is to\ncalculate the clause output as per Eq. 3. The weight of a clause"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3713-934e-11ee-9839-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 4, "chroma:document": "is only updated if the clause output cj(n) is 1 and the clause\nhas been selected for feedback (p j=1). Then the polarity of\nthe clause and the class label ydecide the type of feedback\ngiven. That is, like a regular TM, positive polarity clauses\nreceive Type Ia feedback if the clause output is a true positive\nand Type II feedback if the clause output is a false positive.\nFor clauses with negative polarity, the feedback types switch\nroles.\nWhen clauses receive Type Ia or Type II feedback, their"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3714-934e-11ee-8fff-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 4, "chroma:document": "weights are updated accordingly. We use the stochastic\nsearching on the line (SSL) automaton to learn appropri-\nate weights. SSL is an optimization scheme for unknown\nstochastic environments pioneered by Oommen [31]. The\ngoal is to \ufb01nd an unknown location \u03bb\u2217within a search interval\n[0,1]. In order to \ufb01nd \u03bb\u2217, the only available information for\nthe Learning Mechanism (LM) is the possibly faulty feedback\nfrom its attached environment (E ).\nIn SSL, the search space \u03bbis discretized into Npoints,"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3715-934e-11ee-ac94-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 4, "chroma:document": "{0,1/N,2/N,..., (N\u22121)/N,1}, with Nbeing the dis-\ncretization resolution. During the search, the LM has a loca-\ntion\u03bb\u2208 {0, 1/N, 2/N, ..., (N\u22121)/N, 1}, and can freely\nmove to the left or to the right from its current location. The\nenvironment Eprovides two types of feedback: E=1 is\nthe environment suggestion to increase the value of \u03bbby one\nVOLUME 9, 2021 8237"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3716-934e-11ee-b931-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 5, "chroma:document": "K. D. Abeyrathna et al.: Extending the TM With Integer-Weighted Clauses for Increased Interpretability\nFIGURE 3. The complete learning process of the IWTM in a flowchart.\nstep, and E=0 is the environment suggestion to decrease\nthe value of\u03bbby one step. The next location of \u03bb,\u03bb(n+1)\ncan thus be expressed as follows:\n\u03bb(n+1)={\n\u03bb(n)+1/N,ifE(n)=1,\n\u03bb(n)\u22121/N,ifE(n)=0.(10)\n\u03bb(n+1)={\n\u03bb(n), if\u03bb(n)=1 and E(n)=1,\n\u03bb(n), if\u03bb(n)=0 and E(n)=0.(11)\nAsymptotically, the learning mechanics is able to \ufb01nd a value"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3717-934e-11ee-a74e-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 5, "chroma:document": "arbitrarily close to \u03bb\u2217when N\u2192\u221e andn\u2192\u221e.\nIn our case, the search space of clause weights is [0, \u221e],\nso we use resolution N=1, with no upper bound for \u03bb.\nAccordingly, we operate with integer weights. As seen in\nFIGURE 3, if the clause output is a true positive, we simply\nincrease the weight by 1. Conversely, if the clause output is a\nfalse positive, we decrease the weight by 1.By following the above procedure, the goal is to make\nlow precision clauses team up by giving them low weights,"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3718-934e-11ee-88e2-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 5, "chroma:document": "so that they together can reach the summation target T.\nBy teaming up, precision increases due to the result-\ning ensemble effect. Clauses with high precision, how-\never, gets a higher weight, allowing them to operate more\nindependently.\nThe above weighting scheme has several advantages. First\nof all, increment and decrement operations on integers are\ncomputationally less costly than multiplication based updates\nof real-valued weights. Additionally, a clause with an inte-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3719-934e-11ee-beac-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 5, "chroma:document": "ger weight can be seen as multiple copies of the same\nclause, making it more interpretable than real-valued weight-\ning, as studied in the next section. Additionally, clauses\ncan be turned completely off by setting their weights to\n0 if they do not contribute positively to the classi\ufb01cation\ntask.\n8238 VOLUME 9, 2021"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e371a-934e-11ee-aacc-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 6, "chroma:document": "K. D. Abeyrathna et al.: Extending the TM With Integer-Weighted Clauses for Increased Interpretability\nAlgorithm 1 The Complete IWTM Learning Process\n1:Input: Training data (X, y),m,T,s\n2:Initialize: Random initialization of TAs\n3:Begin: nthtraining round\n4:forj=1,..., mdo if pj=1\u22bfEq. (6) and (9)\n5: if(y=1andjis odd) or(y=0andjis even) then\n6: ifcj=1then \u22bfEq. (3)\n7: wj(n+1)\u2190wj(n)+1\u22bfEq. (10-11)\n8: forfeature k=1,..., 2odo\n9: iflk=1then\n10: Type Ia Feedback\n11: else:\n12: Type Ib Feedback"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e371b-934e-11ee-b538-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 6, "chroma:document": "11: else:\n12: Type Ib Feedback\n13: end if\n14: end for\n15: else:\n16: wj(n+1)\u2190wj(n)\u22bfEq. (10-11)\n17: Type Ib Feedback\n18: end if\n19: else: (y=1andjis even) or(y=0andjis odd)\n20: ifcj=1then \u22bfEq. (3)\n21: ifwj(n)>0then\n22: wj(n+1)\u2190wj(n)\u22121\u22bfEq. (10-11)\n23: end if\n24: forfeature k=1,..., 2odo\n25: iflk=0then\n26: Type II Feedback\n27: else:\n28: Inaction\n29: end if\n30: end for\n31: else:\n32: wj(n+1)\u2190wj(n)\u22bfEq. (10-11)\n33: Inaction\n34: end if\n35: end if\n36:end for\nE. COMPUTATIONAL COMPLEXITY OF THE IWTM"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e371c-934e-11ee-9641-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 6, "chroma:document": "E. COMPUTATIONAL COMPLEXITY OF THE IWTM\nTo evaluate computational complexity, we introduce the three\nconstants\u03b1,\u03b2, and\u03b3, where\u03b1represents the computational\ncost to perform the conjunction of two bits, \u03b2is the com-\nputational cost of computing the summation of two integers,\nand\u03b3is the computational cost to update the state of a single\nautomaton (TA or SSL) in IWTM.\nWe here consider worst-case computational costs for train-\ning and testing, assuming all the TAs in all of the clauses"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e371d-934e-11ee-86bc-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 6, "chroma:document": "are operative and updated. In a TM with mclauses and\nwhen the input vector consists of ofeatures, the TM per-\nforms 2o\u00d7mnumber of TA updates for a single training\nsample. In the IWTM, this becomes (2o +1)\u00d7mupdates\ndue to the weights. Hence, we compute the computational\ncost of updating TA states during the IWTM training as\n\u03b3\u00d7(2o+1)\u00d7m. The cost is simply dtimes higher whenthere are dnumber of training samples in the dataset, i.e.,\nd\u00d7\u03b3\u00d7(2o+1)\u00d7m."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e371e-934e-11ee-b63c-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 6, "chroma:document": "d\u00d7\u03b3\u00d7(2o+1)\u00d7m.\nThe other two TM operations are to compute the output of\nclauses and to sum up the outputs to get the vote difference.\nAssuming all the TAs in all of the clauses have decided to\ninclude their corresponding literals in the clause, the com-\nputational cost for obtaining the clause outputs becomes\n\u03b1\u00d72o\u00d7m. Once the clause outputs are ready, the vote\ndifference is calculated. This requires a computational cost of\n\u03b2\u00d7(m\u22121). We only encounter the above stated computational"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e371f-934e-11ee-996c-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 6, "chroma:document": "requirements during the testing phase. However, both these\ncomponents have to be multiplied with the number of samples\nto obtain the training cost, i.e., d[\u03b1\u00d72o\u00d7m+\u03b2\u00d7(m\u22121)].\nAccordingly, we can formulate the computational com-\nplexity as a function of dwhich exhibits how the com-\nputational complexity of the IWTM varies with dduring\nthe IWTM training. Combining the costs of updating TAs,\ncomputing clause outputs, and calculating the vote difference,\nwe get a linear function f(d):"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3720-934e-11ee-9e3f-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 6, "chroma:document": "we get a linear function f(d):\nf(d)=d[\u03b3\u00d7(2o+1)\u00d7m+\u03b1\u00d72o\u00d7m+\u03b2\u00d7(m\u22121)].\nThen, using the Big O notation [36], the computational com-\nplexity of IWTM training becomes O(d), which means that\nthe complexity of the IWTM increases linearly with the\nnumber of training samples d. Similarly, complexity grows\nlinearly with the number of clauses mand with the number of\ninputs o.\nIII. EMPIRICAL EVALUATION\nIn this section, we empirically evaluate the impact of integer"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3721-934e-11ee-91d0-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 6, "chroma:document": "weighting on the TM using \ufb01ve real-world datasets. Three\nof these datasets are from the health sector: Breast Cancer\ndataset, Liver Disorder dataset, and Heart Disease dataset,\nwhile the two other ones are the Balance Scale andCorporate\nBankruptcy datasets. We use the latter dataset to examine\ninterpretability more closely.\nIn the comparison, the IWTM is compared with the vanilla\nTM as well as the TM with real-valued weights (RWTM).\nAdditionally, we contrast performance against the standard"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3722-934e-11ee-9a3b-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 6, "chroma:document": "machine learning techniques Arti\ufb01cial Neural Networks\n(ANNs), Support Vector Machines (SVMs), Decision Trees\n(DTs), K-Nearest Neighbor (KNN), Random Forest (RF),\nGradient Boosted Trees (XGBoost) [37], Explainable Boost-\ning Machines (EBMs) [32] along with two recent state-of-the-\nart machine learning approaches: Neural Additive Models\n[11] and StructureBoost [34]. For comprehensiveness, three\nANN architectures are used: ANN-1 \u2013 with one hidden layer"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3723-934e-11ee-9928-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 6, "chroma:document": "of 5 neurons; ANN-2 \u2013 with two hidden layers of 20 and 50\nneurons each, and ANN-3 \u2013 with three hidden layers and 20,\n150, and 100 neurons.\nIn the experiments, we use the binarization scheme based\non thresholding proposed in [30] for continuous and categor-\nical features. The results are average measures over 50 inde-\npendent experiment trials. We used 80% of the data for\ntraining and 20% for testing. Hyperparameters were set using\nmanual binary search.\nVOLUME 9, 2021 8239"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3724-934e-11ee-a72d-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 7, "chroma:document": "K. D. Abeyrathna et al.: Extending the TM With Integer-Weighted Clauses for Increased Interpretability\nTABLE 1. Binarizing categorical features in the Bankruptcy dataset.\nTABLE 2. Clauses produced by TM, RWTM, and IWTM for m=10.\nA. BANKRUPTCY\nIn \ufb01nance, accurate prediction of bankruptcy is important to\nmitigate economic loss [38]. However, since the decisions\nmade related to bankruptcy can have critical consequences,\ninterpretable machine learning algorithms are often preferred"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3725-934e-11ee-8000-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 7, "chroma:document": "over black-box methods.\nConsider the historical records of 250 companies in\nthe Bankruptcy dataset.1Each record consists of six fea-\ntures pertinent to predicting bankruptcy: 1) Industrial Risk,\n2) Management Risk, 3) Financial Flexibility, 4) Credibility,\n5) Competitiveness, and 6) Operation Risk. These are cate-\ngorical features where each feature can be in one of three\nstates: Negative (N), Average (A), or Positive (P). The two\ntarget classes are Bankruptcy and Non-bankruptcy. While"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3726-934e-11ee-a696-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 7, "chroma:document": "the class output is binary, the features are ternary. We thus\nbinarize the features using thresholding [30], as shown in\nTABLE 1. Thus, the binarized dataset contains 18 binary\nfeatures.\nWe \ufb01rst investigate the behavior of TM, RWTM, and\nIWTM with very few clauses (10 clauses). The clauses pro-\nduced are summarized in TABLE 2.\nFive out of the ten clauses (clauses with odd index) vote\nfor class 1 and the remaining \ufb01ve (those with even index)"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3727-934e-11ee-ae95-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 7, "chroma:document": "vote for class 0. In the TM, the \ufb01rst clause contains just\none literal, which is the negation of feature 11. From the\nbinarized feature set, we recognize that the 11thfeature\nisNegative Credibility. Likewise, clauses 2, 4, 6, 8, 10\ncontain the same two literals \u2013 the negation of Average\nCompetitiveness andNegative Competitiveness non-negated.\nThe clauses 3, 5, and 9, on the other hand, include Nega-\ntive Competitiveness negated. There is also a free vote for"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3728-934e-11ee-b101-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 7, "chroma:document": "class 1 from the \u2018\u2018empty\u2019\u2019 clause 7, which is ignored during\nclassi\ufb01cation.\n1Available from https://archive.ics.uci.edu/ml/datasets/qualitative_\nbankruptcy.TABLE 3. Clauses produced by TM, RWTM, and IWTM for m=2.\nThe clause outputs of the TM in TABLE 2are visualized\nin FIGURE 4. From the \ufb01gure, it is clear how the trained TM\noperates. It uses only two features, Credibility andCompet-\nitiveness, and their negations. Further, observe how the TM"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3729-934e-11ee-bc85-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 7, "chroma:document": "implicitly introduces weighting by duplicating the clauses.\nTABLE 2also contains the clauses learnt by RWTM and\nIWTM. The most notable difference is that RWTM puts little\nemphasis on the clauses for class 0, giving them weight\n0.0001. Further, it puts most emphasis on the negation of Neg-\native Credibility andNegative Competitiveness. The IWTM,\non the other hand, like the TM, focuses on the negation of\nAverage Competitiveness and non-negated Negative Compet-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e372a-934e-11ee-9d73-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 7, "chroma:document": "itiveness. Note also that, without loss of accuracy, IWTM\nsimpli\ufb01es the set of rules by turning off negated Negative\nCredibility by giving clause 5 weight zero. The three literals\nremaining are the negation of Average Competitiveness and\nNegative Competitiveness , negated and non-negated. Because\nNegative Competitiveness implies negated Average Compet-\nitiveness, IWTM ends up with the simple classi\ufb01cation rule\n(ignoring the weights):\nOutcome={\nBankruptcy ifNegative Competitiveness"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e372b-934e-11ee-95d0-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 7, "chroma:document": "Outcome={\nBankruptcy ifNegative Competitiveness\nNon-bankruptcy otherwise.\n(12)\nBy asking the TMs to only produce two clauses, we can\nobtain the above rule more directly, as shown in TABLE 3.\nAs seen, again, TM, RWTM, and IWTM achieve similar\naccuracy. Further, IWTM turns off Negative Competitive-\nness negated, producing the simplest rule set of the three\napproaches.\nThe previous accuracy results represent the majority of\nexperiment trials. However, some of the trials fail to reach"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e372c-934e-11ee-bea2-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 7, "chroma:document": "an optimal TM con\ufb01guration. Instead of re-running learning\na few times, one can increase the number of clauses for\nincreased robustness in every trial. This comes at the cost of\nreduced interpretability, however. TABLE 4, TABLE 5, and\nTABLE 6contain average performance (Precision, Recall,\nF1-Score, Accuracy, Speci\ufb01city) over 50 experiment trials,\nshowing how robustness increases with more clauses, up to a\ncertain point.\nTABLE 4reports the results for a standard TM. Our goal is"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e372d-934e-11ee-896a-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 7, "chroma:document": "to maximize F1-Score, since accuracy can be misleading for\nimbalanced datasets. Notice how the F1-Score increases with\nthe number of clauses, peaking when mequals 2000. At this\npoint, the average number of literals (include actions) across\nthe clauses is 3622 (rounded to nearest integer). The RWTM\nbehaves similarly, as seen in TABLE 5. However, it peaks\nwith an F1-Score of 0.999 at m=2000. Then 3478 literals\nhave been included on average.\n8240 VOLUME 9, 2021"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e372e-934e-11ee-b98f-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 8, "chroma:document": "K. D. Abeyrathna et al.: Extending the TM With Integer-Weighted Clauses for Increased Interpretability\nFIGURE 4. TM classification process for the Bankruptcy dataset.\nTABLE 4. Performance of TM on Bankruptcy dataset.\nTABLE 5. Performance of RWTM on Bankruptcy dataset.\nTABLE 6. Performance of IWTM on Bankruptcy dataset.\nThe IWTM, on the other hand, achieves its best F1-Score\nwhen mis 500. At that point, an average of 379 literals are\nincluded (only considering clauses with a weight larger than"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e372f-934e-11ee-9229-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 8, "chroma:document": "0), which is signi\ufb01cantly smaller than what was obtained with\nTM and RWTM.\nHow the number of literals increases with the number\nof clauses is shown in FIGURE 5. The IWTM consistently\nFIGURE 5. The number of literals included in different TM setups to work\nwith Bankruptcy dataset.\nproduces fewer literals than the other two schemes, and the\ndifference increases with the number of clauses.\nWe \ufb01nally compare the performance of TM, RWTM, and\nIWTM against several standard machine learning algorithms,"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3730-934e-11ee-8432-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 8, "chroma:document": "namely ANN, DT, SVM, KNN, RF, XGBoost, and EBM.\nThe performance of all of the techniques is compiled in\nTABLE 7. The best F1-Score is obtained by RWTM and\nIWTM, which produce identical results expect that IWTM\nuses fewer literals, less memory during training, and less\ntraining time per epoch. Also, in terms of Accuracy, RWTM\nand IWTM obtains the best average results. Further notice\nthat all of the TMs achieve a Recall of 1.0. Additionally,\nVOLUME 9, 2021 8241"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3731-934e-11ee-a670-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 9, "chroma:document": "K. D. Abeyrathna et al.: Extending the TM With Integer-Weighted Clauses for Increased Interpretability\nTABLE 7. Performance comparison for Bankruptcy dataset.\nFIGURE 6. Sample complexity analysis for the Bankruptcy dataset.\nonly DT, TM, and IWTM require memory close to zero, both\nduring training and testing. RWTM uses more memory in\ntraining than the other two TMs since it has to represent the\nweights of those 2000 clauses as \ufb02oating point numbers."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3732-934e-11ee-9342-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 9, "chroma:document": "We also perform a sample complexity analysis for all\nthe techniques. As FIGURE 6 manifests, all the techniques\nexcept RF and XGBoost surpasses an F1-Score of 0 .975 when\ntraining on 40 percent of the data. The F1-Scores of RF and\nXGBoost start relatively low and \ufb02uctuate around 0.950 after\n40% of the training samples have been processed.\nB. BALANCE SCALE\nFor the remaining datasets, we focus on TM, RWTM and\nIWTM con\ufb01gurations that provide robust performance over"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3733-934e-11ee-8c87-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 9, "chroma:document": "interpretability, comparing with selected machine learning\ntechniques.\nWe \ufb01rst cover the Balance Scale dataset,2which contains\nthree classes: balance scale tip to the right, tip to the left, or is\nin balance. The dataset also contains four features: 1) size of\nthe weight on the left-hand side, 2) distance from the center to\nthe weight on the left, 3) size of the weight on the right-hand\nside, and 4) distance from the center to the weight on the right."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3734-934e-11ee-b5e7-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 9, "chroma:document": "To make the output binary, we remove the \u2018\u2018balanced\u2019\u2019 class\nending up with 576 data samples.\nTABLE 8, TABLE 9, and TABLE 10contain the results of\nTM, RWTM, and IWTM, respectively, with varying m. For\n2Available from http://archive.ics.uci.edu/ml/datasets/balance+scale.TABLE 8. Performance of TM on Balance Scale dataset.\nTABLE 9. Performance of RWTM on Balance Scale dataset.\nTABLE 10. Performance of IWTM on Balance Scale dataset."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3735-934e-11ee-8d76-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 9, "chroma:document": "FIGURE 7. The number of literals included in different TM setups to work\nwith Balance Scale dataset.\nthe TM, F1-Score peaks at 0.945 when m=200. At the\npeak, 790 literals are used on average. RWTM obtains its\n8242 VOLUME 9, 2021"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3736-934e-11ee-b12d-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 10, "chroma:document": "K. D. Abeyrathna et al.: Extending the TM With Integer-Weighted Clauses for Increased Interpretability\nTABLE 11. Performance comparison for Balance Scale dataset.\nFIGURE 8. Sample complexity analysis for the Balance Scale dataset.\nbest F1-Score with 500 clauses, using an average of 4406\nliterals overall. In contrast, IWTM reaches its best F1-Score\nusing only 120 literals, distributed among 100 clauses. Again,\nIWTM uses signi\ufb01cantly fewer literals than TM and RWTM."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3737-934e-11ee-b1b2-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 10, "chroma:document": "The average number of literals used for varying number\nof clauses is plotted in FIGURE 7. IWTM uses the least\nnumber of literals, with the difference increasing with number\nof clauses.\nTABLE 11summarises the performance also of the other\nmachine learning techniques we contrast against. Here, EBM\nobtains the highest F1-Score and Accuracy. Out of the three\nTMs, IWTM achieves the highest F1-Score and Accuracy,\nusing similar or less training memory. The training time"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3738-934e-11ee-bb24-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 10, "chroma:document": "required by IWTM is close to the training time of TM, and\nroughly 60 times less compared to RWTM. According to the\nsample complexity analysis in FIGURE 8, IWTM reaches an\nF1-Score of 0.90 with merely 10% of the training data, and\napproaches 0.95 from 20%.\nC. BREAST CANCER\nThe Breast Cancer dataset3covers recurrence of breast can-\ncer, and consists of nine features: Age, Menopause, Tumor\nSize, Inv Nodes, Node Caps, Deg Malig, Side (left or right),"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3739-934e-11ee-b4f2-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 10, "chroma:document": "the Position of the Breast, and Irradiation Status. The dataset\ncontains 286 patients (201 with non-recurrence and 85 with\n3Available from https://archive.ics.uci.edu/ml/datasets/Breast+CancerTABLE 12. Performance of TM on Breast Cancer dataset.\nTABLE 13. Performance of RWTM on Breast Cancer dataset.\nTABLE 14. Performance of IWTM on Breast Cancer dataset.\nrecurrence). However, some of the patient samples miss some\nof the feature values. These samples are removed from the"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e373a-934e-11ee-a9ae-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 10, "chroma:document": "dataset in the present experiment.\nThe accuracy and number of literals included for\nTM, RWTM, and IWTM are respectively summarized in\nTABLE 12, TABLE 13, and TABLE 14. In contrast to the\nprevious two datasets, the F1-Score peaks at m=2, and then\ndrops with increasing m. For m=2, the average number of\nliterals used by TM, RWTM, and IWTM are 21, 4, and 2,\nrespectively. As seen in FIGURE 9, IWTM requires the least\namount of literals overall.\nThe performance of the other machine learning techniques"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e373b-934e-11ee-849e-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 10, "chroma:document": "is similar in terms of F1-Score, with DT, RF, SVM, XGBoost,\nand EBM providing the worst performance as summarized\nin TABLE 15. The best F1-Score is obtained by TM while\nIWTM provides the second-best. Yet, the moderate increase\nof F1-Score from 0.511 to 0.531 for TM comes at the cost\nof 19 extra literals. The three TMs also uses the least mem-\nory, requiring negligible memory both during training and\nVOLUME 9, 2021 8243"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e373c-934e-11ee-9d0d-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 11, "chroma:document": "K. D. Abeyrathna et al.: Extending the TM With Integer-Weighted Clauses for Increased Interpretability\nTABLE 15. Performance comparison for Breast Cancer dataset.\nFIGURE 9. The number of literals included in different TM setups to work\nwith Breast Cancer dataset.\nFIGURE 10. Sample complexity analysis for the Breast Cancer dataset.\ntesting. Training time per epoch for the TM approaches is also\nsmall, amounting to 0.001 seconds, which is the lowest of"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e373d-934e-11ee-8797-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 11, "chroma:document": "all the algorithms. The TMs also maintain better F1-Scores\nacross all training data sizes in comparison with the other\ntechniques, as seen from the sample complexity analysis in\nFIGURE 10.D. LIVER DISORDERS\nThe Liver Disorders dataset4was created by BUPA Medical\nResearch and Development Ltd. (hereafter \u2018\u2018BMRDL\u2019\u2019) dur-\ning the 1980s as part of a larger health-screening database.\nThe dataset consists of 7 attributes, namely Mean Cor-\npuscular Volume, Alkaline Phosphotase, Alamine Amino-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e373e-934e-11ee-bd1f-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 11, "chroma:document": "transferase, Aspartate Aminotransferase, Gamma-Glutamyl\nTranspeptidase, Number of Half-Pint Equivalents of Alco-\nholic Beverages (drunk per day), and Selector (used to split\ndata into training and testing sets). However, McDermott\nand Forsyth [39] claim that many researchers have used\nthe dataset incorrectly, considering the Selector attribute\nas class label. Based on the recommendation of McDer-\nmott and Forsythof, we here instead use Number of Half-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e373f-934e-11ee-bf28-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 11, "chroma:document": "Pint Equivalents of Alcoholic Beverages as the dependent\nvariable, binarized using the threshold \u22653. The Selec-\ntor attribute is discarded. The remaining attributes repre-\nsent the results of various blood tests, and we use them as\nfeatures.\nTABLE 17, TABLE 18, and TABLE 19summarizes\nthe performance of TM, RWTM, and IWTM, respectively.\nAs seen, all of the TM F1-Scores peak at m=2. TM uses an\naverage of 27 literals, RWTM uses 29, while IWTM uses 9."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3740-934e-11ee-84db-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 11, "chroma:document": "FIGURE 11plots how the number of literals increases with\nnumber of clauses, again con\ufb01rming that IWTM uses fewer\nliterals overall.\nConsidering the other machine learning techniques\n(TABLE 16), RF produces the highest F1-Score 0.729,\nobtained with the smallest memory usage for both training\nand testing. However, this performance is comparable to the\nDT F1-Score of 0 .728, spending negligible testing memory.\nOf the three TM approaches, RWTM obtains the highest"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3741-934e-11ee-8d66-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 11, "chroma:document": "F1-Score - the fourth highest among all of the techniques.\nAll three TMs use insigni\ufb01cant memory during both training\nand testing, requiring the same amount of training time per\nepoch.\n4Available from https://archive.ics.uci.edu/ml/datasets/Liver+Disorders.\n8244 VOLUME 9, 2021"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3742-934e-11ee-a3a1-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 12, "chroma:document": "K. D. Abeyrathna et al.: Extending the TM With Integer-Weighted Clauses for Increased Interpretability\nTABLE 16. Performance comparison for Liver Disorders dataset.\nTABLE 17. Performance of TM on Liver Disorders dataset.\nTABLE 18. Performance of RWTM on Liver Disorders dataset.\nTABLE 19. Performance of IWTM on Liver Disorders dataset.\nWith 10% of the training data available, IWTM obtains the\nhighest F1-Score among all the techniques \u2013 a score of 0.725."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3743-934e-11ee-981b-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 12, "chroma:document": "After that, the score \ufb02uctuates with increasing training data\nsize, as depicted in FIGURE 12.\nE. HEART DISEASE\nThe Heart Disease dataset5concerns prediction of heart dis-\nease. To this end, 13 features are available, selected among\n75. Out of the 13 features, 6 are real-valued, 3 are binary, 3\nare nominal, and one is ordered.\nTABLE 21, TABLE 22, and TABLE 23summarize the per-\nformance of TM, RWTM, and IWTM on the Heart Disease\ndataset. For the TM, the best F1-Score occurs with m=10,"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3744-934e-11ee-9014-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 12, "chroma:document": "achieved by using 346 literals on average. The RWTM F1-\nScore peaks at m=2000 with 18 528 literals. IWTM peaks\n5Available from https://archive.ics.uci.edu/ml/datasets/Statlog+\n%28Heart%29.\nFIGURE 11. The number of literals included in different TM setups to\nwork with the Liver Disorders dataset.\nFIGURE 12. Sample complexity analysis for the Liver Disorders dataset.\natm=10, with slightly lower F1-Score, however, employing\nonly 226 literals on average."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3745-934e-11ee-ace4-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 12, "chroma:document": "only 226 literals on average.\nConsidering the number of literals used with increasing\nnumber of clauses (FIGURE 13), TM and IWTM behave\nsimilarly, while RWTM requires signi\ufb01cantly more literals.\nOut of the considered machine learning models, EBM\nobtains the best F1-Score, while RWTM, IWTM, and ANN-2\nfollow closely behind (TABLE 20). However, EBM needs\nthe highest training time and uses the second largest training\nVOLUME 9, 2021 8245"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3746-934e-11ee-9349-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 13, "chroma:document": "K. D. Abeyrathna et al.: Extending the TM With Integer-Weighted Clauses for Increased Interpretability\nTABLE 20. Performance comparison for Heart Disease dataset.\nTABLE 21. Performance of TM on Heart Disease dataset.\nTABLE 22. Performance of RWTM on Heart Disease dataset.\nTABLE 23. Performance of IWTM on Heart Disease dataset.\nmemory, while all three TMs use negligible memory during\nboth training and testing. Apart from DT, all of the machine"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3747-934e-11ee-ad09-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 13, "chroma:document": "learning models surpass an F1-Score of 0.5 using only 10%\nof the training data, as shown in FIGURE 14. From 30%\nonward, the F1-Score of all the models behaves similarly,\nwith EBM being superior after 60%.\nF. SUMMARY OF EMPIRICAL EVALUATION\nTo compare overall performance of the various techniques,\nwe calculate average F1-Score across the datasets. Further to\nevaluate overall interpretability of TM, RWTM and IWTM,\nwe also report average number of literals used, overall."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3748-934e-11ee-b233-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 13, "chroma:document": "In all brevity, the average F1-Score of ANN-1, ANN-2,\nANN-3, DT, SVM, KNN, RF, TM, XGBoost, EBM, RWTM,\nand IWTM are 0.770, 0.757, 0.744, 0 .742, 0.713, 0.737,\n0.724 0.728, 0.775, 0.762, 0.774, and 0.777, respectively. Out\nof all the considered models, IWTM obtains the best average\nF1-Score, which is 0.777. Also notice that increasing ANN\nmodel complexity (from ANN-1 to ANN-3) reduces overall\nFIGURE 13. The number of literals included in different TM setups to\nwork with Heart Disease dataset."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3749-934e-11ee-bef4-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 13, "chroma:document": "work with Heart Disease dataset.\nFIGURE 14. Sample complexity analysis for the Heart Disease dataset.\nF1-Score, which can potentially be explained by the small\nsize of the datasets.\nThe F1-Score of RWTM is also competitive, however,\nit requires much more literals than IWTM. Indeed, the\naverage number of literals employed are 961 for TM, 17 670\nfor RWTM, and 147 for IWTM. That is, IWTM uses 6.5\ntimes fewer literals than TM, and 120 times fewer literals than\nRWTM."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e374a-934e-11ee-833e-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 13, "chroma:document": "RWTM.\nThe average combined memory requirement (training +\ntesting) by TM, RWTM, and IWTM are 3.27 KB, 79.46 KB,\nand 3.27, respectively. The combined memory usage of\n8246 VOLUME 9, 2021"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e374b-934e-11ee-9381-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 14, "chroma:document": "K. D. Abeyrathna et al.: Extending the TM With Integer-Weighted Clauses for Increased Interpretability\nTABLE 24. Performance (in AUC) comparison against recent\nstate-of-the-art machine learning models.\nIWTM is signi\ufb01cantly less compared to the other models \u2013\nANN-1:\u2248305 times, ANN-2: \u22481 278 times, ANN-3: \u2248\n11 096 times, DT:\u224834 times, SVM:\u2248255 times, KNN:\u2248\n106 times, RF:\u224845 times, XGBoost:\u2248877 times, EBM:\u2248\n1226 times, and RWTM: \u224824 times.\nG. COMPARISON AGAINST RECENT STATE-OF-THE-ART"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e374c-934e-11ee-bc10-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 14, "chroma:document": "G. COMPARISON AGAINST RECENT STATE-OF-THE-ART\nMACHINE LEARNING MODELS\nIn this section, we compare IWTM accuracy with reported\nresults on recent state-of-the-art machine learning mod-\nels. First, we perform experiments on Fraud Detection and\nCOMPAS: Risk Prediction in Criminal Justice datasets to\nstudy the performance of IWTM in comparison with Neu-\nral Additive Models [11]. A Neural Additive Model is a\nnovel member of so-called general adaptive models. In Neu-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e374d-934e-11ee-8e31-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 14, "chroma:document": "ral Additive Models, the signi\ufb01cance of each input feature\ntowards the output is learned by a dedicated neural network.\nDuring the training phase, the complete set of neural networks\nare jointly trained to learn complex interactions between\ninputs and outputs.\nTo compare the performance against StructureBoost [34],\nwe use the CA weather dataset [40]. For simplicity, we use\nonly the CA-58 subset of the dataset in this study. Struc-\ntureBoost is based on gradient boosting and is capable of"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e374e-934e-11ee-8670-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 14, "chroma:document": "exploiting the structure of categorical variables. Structure-\nBoost outperforms established models such as CatBoost and\nLightBoost on multiple classi\ufb01cation tasks [34].\nSince the performance of both of the above techniques has\nbeen measured in terms of Area under the ROC Curve (AUC),\nwe here use a soft TM output layer [41] to calculate AUC. The\nperformance characteristics are summarized in TABLE 24.\nTABLE 24 shows that on Fraud Detection, IWTM outper-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e374f-934e-11ee-95f5-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 14, "chroma:document": "forms NAMs and all the other techniques mentioned in [11].\nOn the COMPAS dataset, IWTM exhibits competitive per-\nformance compared to NAMs, EBM, XGBoost, and DNNs.\nIWTM shows, however, superior performance compared to\nLogistic Regression and DT on COMPAS. The performance\nof IWTM on CA-20 is better in comparison to Structure-\nBoost, LightBoost, and CatBoost models, reported in [34].\nIV. CONCLUSION\nIn this paper, we proposed a novel Tsetlin Machine (TM)"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3750-934e-11ee-b4db-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 14, "chroma:document": "having integer weights attached to clauses, to addressthe accuracy-interpretability challenge in machine learning.\nIn our proposed TM (denoted IWTM), the weights are learnt\nusing the stochastic searching on the line (SSL) automa-\nton. The weights attached to the clauses help the TM to\nthe represent sub-patterns in a more compact way. Since\ninteger weights can turn off unimportant clauses by setting\ntheir weight to 0, this allows the TM to create a classi-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3751-934e-11ee-953a-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 14, "chroma:document": "\ufb01er with fewer number of literals compared to the vanilla\nTM and the Real-Value Weighted TM (RWTM). We have\nprovided empirical evidence by generating rules for several\ndatasets. In conclusion, the IWTM obtains on par or better\naccuracy compared to the vanilla TM and the RWTM while\nusing respectively 6.5 and 125 times fewer literals. Further-\nmore, in terms of average F1-Score, the proposed IWTM\nalso outperforms several state-of-the-art machine learning\nalgorithms."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3752-934e-11ee-9727-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 14, "chroma:document": "algorithms.\nIn our future work, we intend to investigate more advanced\nSSL schemes, such as Continuous Point Location with Adap-\ntive Tertiary Search (CPL-ATS) [42] and Random Walk-\nbased Triple Level Learning Algorithm (RWTLA) [43],\nincluding their impact on interpretability.\nREFERENCES\n[1] C. Molnar, Interpretable Machine Learning . Morrisville, NC, USA: Lulu,\n2019.\n[2] R. Miotto, F. Wang, S. Wang, X. Jiang, and J. T. Dudley, \u2018\u2018Deep learning"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3753-934e-11ee-b3e4-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 14, "chroma:document": "for healthcare: Review, opportunities and challenges,\u2019\u2019 Brie\ufb01ngs Bioinf.,\nvol. 19, no. 6, pp. 1236\u20131246, Nov. 2018.\n[3] B. Baesens, C. Mues, M. De Backer, J. Vanthienen, and R. Setiono,\n\u2018\u2018Building intelligent credit scoring systems using decision tables,\u2019\u2019 in\nEnterprise Information Systems V. Dordrecht, The Netherlands: Springer,\n2004, pp. 131\u2013137.\n[4] J. Huysmans, K. Dejaeger, C. Mues, J. Vanthienen, and B. Baesens,\n\u2018\u2018An empirical evaluation of the comprehensibility of decision table, tree"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3754-934e-11ee-bf78-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 14, "chroma:document": "and rule based predictive models,\u2019\u2019 Decis. Support Syst., vol. 51, no. 1,\npp. 141\u2013154, Apr. 2011.\n[5] R. Bellazzi and B. Zupan, \u2018\u2018Predictive data mining in clinical medicine:\nCurrent issues and guidelines,\u2019\u2019 Int. J. Med. Informat., vol. 77, no. 2,\npp. 81\u201397, Feb. 2008.\n[6] S. Mani, W. R. Shankle, and M. J. Pazzani, \u2018\u2018Acceptance of rules generated\nby machine learning among medical experts,\u2019\u2019 Methods Inf. Med., vol. 40,\nno. 5, pp. 380\u2013385, 2001."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3755-934e-11ee-bd99-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 14, "chroma:document": "no. 5, pp. 380\u2013385, 2001.\n[7] A. A. Freitas, D. C. Wieser, and R. Apweiler, \u2018\u2018On the importance of\ncomprehensible classi\ufb01cation models for protein function prediction,\u2019\u2019\nIEEE/ACM Trans. Comput. Biol. Bioinf., vol. 7, no. 1, pp. 172\u2013182,\nJan. 2010.\n[8] D. Szafron, P. Lu, R. Greiner, D. S. Wishart, B. Poulin, R. Eisner, Z. Lu,\nJ. Anvik, C. Macdonell, A. Fyshe, and D. Meeuwis, \u2018\u2018Proteome analyst:\nCustom predictions with explanations in a Web-based tool for high-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3756-934e-11ee-a40e-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 14, "chroma:document": "throughput proteome annotations,\u2019\u2019 Nucleic Acids Res., vol. 32, no. 2,\npp. W365\u2013W371, Jul. 2004.\n[9] E. Lima, C. Mues, and B. Baesens, \u2018\u2018Domain knowledge integration in data\nmining using decision tables: Case studies in churn prediction,\u2019\u2019 J. Oper.\nRes. Soc., vol. 60, no. 8, pp. 1096\u20131106, Aug. 2009.\n[10] W. Verbeke, D. Martens, C. Mues, and B. Baesens, \u2018\u2018Building compre-\nhensible customer churn prediction models with advanced rule induction"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3757-934e-11ee-ab15-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 14, "chroma:document": "techniques,\u2019\u2019 Expert Syst. Appl., vol. 38, no. 3, pp. 2354\u20132364, Mar. 2011.\n[11] R. Agarwal, N. Frosst, X. Zhang, R. Caruana, and G. E. Hinton, \u2018\u2018Neural\nadditive models: Interpretable machine learning with neural nets,\u2019\u2019 2020,\narXiv:2004.13912. [Online]. Available: http://arxiv.org/abs/2004.13912\n[12] M. T. Ribeiro, S. Singh, and C. Guestrin, \u2018\u2018Why should I trust you?\u2019\nExplaining the predictions of any classi\ufb01er,\u2019\u2019 in Proc. 22nd ACM SIGKDD"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3758-934e-11ee-96fb-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 14, "chroma:document": "Int. Conf. Knowl. Discovery Data Mining, 2016, pp. 1135\u20131144.\n[13] C. Rudin, \u2018\u2018Stop explaining black box machine learning models for high\nstakes decisions and use interpretable models instead,\u2019\u2019 Nature Mach.\nIntell., vol. 1, no. 5, pp. 206\u2013215, May 2019.\nVOLUME 9, 2021 8247"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3759-934e-11ee-909c-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 15, "chroma:document": "K. D. Abeyrathna et al.: Extending the TM With Integer-Weighted Clauses for Increased Interpretability\n[14] R. Agrawal, T. Imieli\u0144ski, and A. Swami, \u2018\u2018Mining association rules\nbetween sets of items in large databases,\u2019\u2019 ACM SIGMOD Rec., vol. 22,\nno. 2, pp. 207\u2013216, Jun. 1993.\n[15] T. McCormick, C. Rudin, and D. Madigan, \u2018\u2018A hierarchical model for\nassociation rule mining of sequential events: An approach to automated\nmedical symptom prediction,\u2019\u2019 MIT Sloan Res. Paper, Jan. 2011. [Online]."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e375a-934e-11ee-9340-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 15, "chroma:document": "Available: https://ssrn.com/abstract=1736062\n[16] V. Feldman, \u2018\u2018Hardness of approximate two-level logic minimization and\nPAC learning with membership queries,\u2019\u2019 J. Comput. Syst. Sci., vol. 75,\nno. 1, pp. 13\u201326, Jan. 2009.\n[17] L. G. Valiant, \u2018\u2018A theory of the learnable,\u2019\u2019 Commun. ACM, vol. 27, no. 11,\npp. 1134\u20131142, Nov. 1984.\n[18] T. Wang, C. Rudin, F. Doshi-Velez, Y. Liu, E. Klamp\ufb02, and P. MacNeille,\n\u2018\u2018A Bayesian framework for learning rule sets for interpretable classi\ufb01ca-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e375b-934e-11ee-9afe-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 15, "chroma:document": "tion,\u2019\u2019 J. Mach. Learn. Res., vol. 18, no. 1, pp. 2357\u20132393, 2017.\n[19] J. R. Hauser, O. Toubia, T. Evgeniou, R. Befurt, and D. Dzyabura, \u2018\u2018Dis-\njunctions of conjunctions, cognitive simplicity, and consideration sets,\u2019\u2019\nJ. Marketing Res., vol. 47, no. 3, pp. 485\u2013496, Jun. 2010.\n[20] Y. Liang and G. Van den Broeck, \u2018\u2018Learning logistic circuits,\u2019\u2019 in Proc. 33rd\nAAAI Conf. Artif. Intell., vol. 33, 2019, pp. 4277\u20134286.\n[21] O.-C. Granmo, \u2018\u2018The tsetlin machine\u2014A game theoretic bandit driven"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e375c-934e-11ee-8d12-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 15, "chroma:document": "approach to optimal pattern recognition with propositional logic,\u2019\u2019 2018,\narXiv:1804.01508. [Online]. Available: http://arxiv.org/abs/1804.01508\n[22] A. Phoulady, O.-C. Granmo, S. R. Gorji, and H. A. Phoulady,\n\u2018\u2018The weighted tsetlin machine: Compressed representations with\nweighted clauses,\u2019\u2019 2019, arXiv:1911.12607. [Online]. Available:\nhttps://arxiv.org/abs/1911.12607\n[23] G. T. Berge, O.-C. Granmo, T. O. Tveit, M. Goodwin, L. Jiao, and"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e375d-934e-11ee-a5bc-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 15, "chroma:document": "B. V. Matheussen, \u2018\u2018Using the tsetlin machine to learn human-interpretable\nrules for high-accuracy text categorization with medical applications,\u2019\u2019\nIEEE Access, vol. 7, pp. 115134\u2013115146, 2019.\n[24] K. D. Abeyrathna, O.-C. Granmo, X. Zhang, L. Jiao, and M. Goodwin,\n\u2018\u2018The regression Tsetlin machine: A novel approach to interpretable nonlin-\near regression,\u2019\u2019 Phil. Trans. Roy. Soc. A, Math., Phys. Eng. Sci., vol. 378,\nno. 2164, Feb. 2020, Art. no. 20190165."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e375e-934e-11ee-aac1-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 15, "chroma:document": "no. 2164, Feb. 2020, Art. no. 20190165.\n[25] S. R. Gorji, O.-C. Granmo, A. Phoulady, and M. Goodwin, \u2018\u2018A Tsetlin\nmachine with multigranular clauses,\u2019\u2019 in Proc. 39th Int. Conf. Innov.\nTechn. Appl. Artif. Intell. (SGAI), in Lecture Notes in Computer Science,\nvol. 11927. Cham, Switzerland: Springer, 2019, pp. 146\u2013151.\n[26] S. Gorji, O. C. Granmo, S. Glimsdal, J. Edwards, and M. Goodwin,\n\u2018\u2018Increasing the inference and learning speed of Tsetlin machines with"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e375f-934e-11ee-9295-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 15, "chroma:document": "clause indexing,\u2019\u2019 in Proc. Int. Conf. Ind., Eng. Other Appl. Appl. Intell.\nSyst. Cham, Switzerland: Springer, 2020, pp. 695\u2013708.\n[27] A. Wheeldon, R. Sha\ufb01k, A. Yakovlev, J. Edwards, I. Haddadi, and\nO.-C. Granmo, \u2018\u2018Tsetlin machine: A new paradigm for pervasive AI,\u2019\u2019\ninProc. SCONA Workshop Design, Automat. Test Eur. (DATE), 2020.\n[Online]. Available: https://www.date-conference.com/node/468\n[28] M. L. Tsetlin, \u2018\u2018On behaviour of \ufb01nite automata in random medium,\u2019\u2019"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3760-934e-11ee-89de-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 15, "chroma:document": "Avtomatika I Telemekhanika, vol. 22, no. 10, pp. 1345\u20131354, 1961.\n[29] O.-C. Granmo, S. Glimsdal, L. Jiao, M. Goodwin, C. W. Omlin, and\nG. T. Berge, \u2018\u2018The convolutional Tsetlin machine,\u2019\u2019 2019,\narXiv:1905.09688. [Online]. Available: http://arxiv.org/abs/1905.09688\n[30] K. D. Abeyrathna, O.-C. Granmo, X. Zhang, and M. Goodwin, \u2018\u2018A scheme\nfor continuous input to the Tsetlin machine with applications to forecasting\ndisease outbreaks,\u2019\u2019 in Proc. Int. Conf. Ind., Eng. Other Appl. Appl. Intell."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3761-934e-11ee-94e8-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 15, "chroma:document": "Syst. Cham, Switzerland: Springer, 2019, pp. 564\u2013578.\n[31] B. J. Oommen, \u2018\u2018Stochastic searching on the line and its applications to\nparameter learning in nonlinear optimization,\u2019\u2019 IEEE Trans. Syst. Man,\nCybern. B, Cybern., vol. 27, no. 4, pp. 733\u2013739, Aug. 1997.\n[32] H. Nori, S. Jenkins, P. Koch, and R. Caruana, \u2018\u2018InterpretML:\nA uni\ufb01ed framework for machine learning interpretability,\u2019\u2019 2019,\narXiv:1909.09223. [Online]. Available: http://arxiv.org/abs/1909.09223"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3762-934e-11ee-a637-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 15, "chroma:document": "[33] Y. Lou, R. Caruana, and J. Gehrke, \u2018\u2018Intelligible models for classi\ufb01cation\nand regression,\u2019\u2019 in Proc. 18th ACM SIGKDD Int. Conf. Knowl. Discovery\nData Mining (KDD), 2012, pp. 150\u2013158.\n[34] B. Lucena, \u2018\u2018StructureBoost: Ef\ufb01cient gradient boosting for structured\ncategorical variables,\u2019\u2019 2020, arXiv:2007.04446. [Online]. Available:\nhttp://arxiv.org/abs/2007.04446\n[35] K. S. Narendra and M. A. Thathachar, Learning Automata: An Introduc-\ntion. Chelmsford, MA, USA: Courier Corporation, 2012."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3763-934e-11ee-a67f-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 15, "chroma:document": "[36] I. Chivers and J. Sleightholme, \u2018\u2018An introduction to algorithms and the\nbig O notation,\u2019\u2019 in Introduction to Programming With Fortran. Cham,\nSwitzerland: Springer, 2015, pp. 359\u2013364.[37] T. Chen and C. Guestrin, \u2018\u2018XGBoost: A scalable tree boosting system,\u2019\u2019\ninProc. 22nd ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining,\nAug. 2016, pp. 785\u2013794.\n[38] M.-J. Kim and I. Han, \u2018\u2018The discovery of experts\u2019 decision rules from"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3764-934e-11ee-89f6-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 15, "chroma:document": "qualitative bankruptcy data using genetic algorithms,\u2019\u2019 Expert Syst. Appl.,\nvol. 25, no. 4, pp. 637\u2013646, Nov. 2003.\n[39] J. McDermott and R. S. Forsyth, \u2018\u2018Diagnosing a disorder in a classi\ufb01cation\nbenchmark,\u2019\u2019 Pattern Recognit. Lett., vol. 73, pp. 41\u201343, Apr. 2016.\n[40] B. Lucena, \u2018\u2018Exploiting categorical structure using tree-based meth-\nods,\u2019\u2019 2020, arXiv:2004.07383. [Online]. Available: http://arxiv.org/\nabs/2004.07383\n[41] K. D. Abeyrathna, O.-C. Granmo, and M. Goodwin, \u2018\u2018On obtaining classi-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3765-934e-11ee-b11e-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 15, "chroma:document": "\ufb01cation con\ufb01dence, ranked predictions and AUC with Tsetlin machines,\u2019\u2019\ninProc. IEEE Symp. Series Comput. Intell. (ISSC), 2020.\n[42] B. J. Oommen and G. Raghunath, \u2018\u2018Automata learning and intelligent\ntertiary searching for stochastic point location,\u2019\u2019 IEEE Trans. Syst. Man,\nCybern. B, Cybern., vol. 28, no. 6, pp. 947\u2013954, Dec. 1998.\n[43] W. Jiang, D.-S. Huang, and S. Li, \u2018\u2018Random walk-based solution to triple\nlevel stochastic point location problem,\u2019\u2019 IEEE Trans. Cybern., vol. 46,"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3766-934e-11ee-98d7-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 15, "chroma:document": "no. 6, pp. 1438\u20131451, Jun. 2016.\nK. DARSHANA ABEYRATHNA received the\nB.Sc. degree in mechatronics engineering from\nAIT University, Thailand, in 2015, and the M.Sc.\ndegree from the Big Data Research Group, Tham-\nmasat University, Thailand, in 2017. He is cur-\nrently a Ph.D. Research Fellow with the University\nof Agder. He is also with CAIR, working on a\nproject which develops a global grid that facilitates\nreal-time compilation, and management and anal-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3767-934e-11ee-b2c2-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 15, "chroma:document": "real-time compilation, and management and anal-\nysis of spatio-temporal data. His research interests\ninclude arti\ufb01cial neural networks, data mining, optimization, and operations\nresearch.\nOLE-CHRISTOFFER GRANMO received the\nmaster\u2019s and Ph.D. degrees from the University\nof Oslo, Norway, in 1999 and 2004, respectively.\nHe is currently a Professor with the University\nof Agder and the Founding Director of the Cen-\ntre for Arti\ufb01cial Intelligence Research (CAIR)."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3768-934e-11ee-8549-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 15, "chroma:document": "tre for Arti\ufb01cial Intelligence Research (CAIR).\nHe has authored in excess of 140 refereed arti-\ncles within machine learning, encompassing learn-\ning automata, bandit algorithms, Tsetlin machines,\nBayesian reasoning, reinforcement learning, and\ncomputational linguistics. Apart from his academic endeavors, he co-\nfounded the company Anzyz Technologies AS. He is also a Co-Founder of\nthe Norwegian Arti\ufb01cial Intelligence Consortium (NORA).\nMORTEN GOODWIN received the master\u2019s"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e3769-934e-11ee-a3d2-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 15, "chroma:document": "MORTEN GOODWIN received the master\u2019s\ndegree from the University of Agder, Norway,\nin 2005, and the Ph.D. degree from the Depart-\nment of Computer Science, Aalborg University,\nDenmark, in 2011, where he applied machine\nlearning algorithms on eGovernment indicators\nwhich are dif\ufb01cult to measure automatically. He is\ncurrently an Associate Professor with the Univer-\nsity of Agder. His research interests include data-\nmining, optimization, machine learning, swarm"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e376a-934e-11ee-a168-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\Extending_the_Tsetlin_Machine_With_Integer-Weighted_Clauses_for_Increased_Interpretability.pdf", "page": 15, "chroma:document": "mining, optimization, machine learning, swarm\nintelligence, deep learning, and adaptive learning. He has written more\nthan 40 peer-reviewed scienti\ufb01c publications in this area and supervised more\nthan 60 student projects at the master\u2019s and Ph.D. level.\n8248 VOLUME 9, 2021"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fbf-934e-11ee-9c6f-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 0, "chroma:document": "(2022)52:\u20131746517489 AppliedIntelligence\nhttps://doi.org/10.1007/s10489-022-03281-1\nWord-levelhumaninterpretablescoringmechanismfornoveltext\ndetectionusingTsetlinMachines\nBimalBhattarai1\u00b7Ole-Christo\ufb00erGranmo1\u00b7LeiJiao1\nAccepted:20January2022\n\u00a9TheAuthor(s)2022\nAbstract\nRecent research in novelty detection focuses mainly on document-level classification, employing deep neural networks"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fc0-934e-11ee-a257-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 0, "chroma:document": "(DNN). However, the black-box nature of DNNs makes it difficult to extract an exact explanation of why a document is\nconsidered novel. In addition, dealing with novelty at the word level is crucial to provide a more fine-grained analysis than\nwhat is available at the document level. In this work, we propose a Tsetlin Machine (TM)-based architecture for scoring\nindividual words according to their contribution to novelty. Our approach encodes a description of the novel documents using"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fc1-934e-11ee-8244-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 0, "chroma:document": "the linguistic patterns captured by TM clauses. We then adapt this description to measure how much a word contributes to\nmaking documents novel. Our experimental results demonstrate how our approach breaks down novelty into interpretable\nphrases, successfully measuring novelty.\nKeywords Natural language processing \u00b7Novelty detection \u00b7Tsetlin Machine \u00b7Interpretable learning \u00b7Explainable AI\n1Introduction\nThe fundamental principle underlying machine learning"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fc2-934e-11ee-9eb5-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 0, "chroma:document": "classifiers is a generalization \u2013 the ability to form a decision\nboundary that differentiates new input into known classes.\nWhen training a supervised classifier, it is common to\nassume that the classes to be recognized are present both\nin the training and test data [ 49]. However, given an\nopen world, training on all conceivable classes of input is\nimpractical. This problem introduces the need for novelty\ndetection \u2013 the task of spotting input classes that one has"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fc3-934e-11ee-9ae5-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 0, "chroma:document": "not seen before. The problem is particularly severe in text-\nbased supervised classification due to the many-faceted\nnature of natural language, which gives rise to multiple\napplication-dependent interpretations. Indeed, researchers\n/envelopebackBimal Bhattarai\nbimal.bhattarai@uia.no; bobsbimal58@gmail.com\nOle-Christoffer Granmo\nole.granmo@uia.no\nLei Jiao\nlei.jiao@uia.no\n1Centre for Artificial Intelligence Research (CAIR), University"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fc4-934e-11ee-a1e4-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 0, "chroma:document": "of Agder, Grimstad, Norwayhave for a long time tried to address novelty detection in\nnatural language. So far, no single best model has appeared.\nIndeed, the success of each model relies on the properties of\neach particular dataset.\nThe problem of novelty detection arises in many tasks,\nsuch as fault detection [ 16] and handwritten alphabet\nrecognition [ 54]. In general, one applies novelty detection\nwhen it is required to know whether a given input is similar"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fc5-934e-11ee-b3a2-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 0, "chroma:document": "to or significantly different from the training data. For\nnatural language text, the novelty detector should discern\nthat a text does not belong to a predefined set of topics.\nSeveral challenges make such novelty detection particularly\ndifficult:\n1. Textual information tends to be diverse, composed of\nlarge vocabularies.\n2. Language and topics are typically evolving, making the\nnovelty detection problem dynamic [ 21].\nLately, the aforementioned challenges have manifested"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fc6-934e-11ee-a858-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 0, "chroma:document": "when using supervised learning to build chatbots, an\napplication area that is gaining traction. A chatbot typically\nneeds to handle the language of a multitude of users with\nevolving information requirements. As such, it must be able\nto determine when it is capable of answering a query and\nwhen it faces a new topic.\nMajority of the existing literature on text-based novelty\ndetection addresses one of the following granularity levels:/Published online: 2 April 2022"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fc7-934e-11ee-9fdd-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 1, "chroma:document": "B.Bhattaraietal.\n1. Event-level techniques [ 4] perform topic detection and\ntracking on a stream of documents.\n2. Document-level techniques [ 17] classify an incoming\ndocument as known or novel based on its content.\n3. Sentence-level techniques [ 6] look for novel sentences\nwithin a particular document.\nUsually, the sentences/documents are ranked based on\nsome sort of similarity score, obtained from comparing them\nwith previously seen sentences/documents. For instance, the"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fc8-934e-11ee-944a-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 1, "chroma:document": "Maximal Marginal Relevance model (MMR) proposed in\n[14] assigns low scores to previously seen sentences/docu-\nments, while assigning high scores to novel ones.\nFigure 1illustrates the problem of novelty detection,\ncontrasting it against anomaly and outlier detection.\nAnomaly detection [ 15] concerns discovering anomalies,\nwhich are invalid data points. Outlier detection [ 3,29],\non the other hand, flags legitimate data points that deviate"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fc9-934e-11ee-b6c5-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 1, "chroma:document": "significantly from the mean. Finally, novelty detection [ 43]\nis the discovery of entirely new types of data points.\nIn contrast to prior work, we here focus on novelty\ndetection at the word level. To this end, we propose a\nnew interpretable machine learning approach for calculating\nnovelty scores for the words within a sentence. The\ncalculation is based on the linguistic patterns captured by\na Tsetlin Machine (TM) in the form of AND-rules (i.e.,"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fca-934e-11ee-af4b-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 1, "chroma:document": "conjunctive clauses). To the best of our knowledge, this is\nthe first study of its kind on this problem.\nProblemde\ufb01nition In the supervised classification setting,\nipre-labeled data points D={(v1,y1), (v 2,y2) ,...,\n(vi,yi)}is used for training. Here, viis the ithinput\nexample and yiis its class. The input viis ant-dimensional\nreal-valued vector (x1,x2,...,x o)\u2208Rt,w h e r e xorefers\nto the othelement of the vector. The class yi\u2208Y=\n{1,2,...,C l}, in turn, is an integer class index referring"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fcb-934e-11ee-8ba7-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 1, "chroma:document": "to one out of Clclasses. Learning a classifier entails\nconstructing a classification function f( v;D),f:Rt\u2192Y,\nbased on the data D. The function simply assigns a label y\nto the data point v. Our emphasis is novelty scoring, which\ncan be seen as another function z(v;D),z :Rt\u2192R.The function computes a real-valued novelty score for input\ndata point v, with the purpose of discerning new classes not\nfound in Y. In this way, a classifier can return the correct"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fcc-934e-11ee-82c6-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 1, "chroma:document": "class label while flagging novel examples. Considering each\nelement in vto represent a specific word, this paper further\nextend the novelty detection by introducing a method for\nbreaking down the overall score z(v;D)forvinto the\ncontribution of each element xo. By doing so, we break\ndown novelty into interpretable phrases.\nPaper contributions In this paper, we use the TM to con-\nstruct conjunctive clauses in propositional logic. In this"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fcd-934e-11ee-b5b9-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 1, "chroma:document": "manner, we capture frequent patterns in the data D,w h i c h\nwe then utilize to characterize the known classes Ycom-\nprehensively. The novelty score is then calculated based on\nexamining the clauses that match the given input. By fur-\nther looking into the composition of each clause, we are\nable to break down the novelty score into the contribu-\ntion of the different phrases. This decomposition is based\non training clauses for the novel data and then measuring"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fce-934e-11ee-8760-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 1, "chroma:document": "the relative frequency of each word inside the clauses for\nthe known classes, contrasted against the relative frequency\nobtained from the clauses of the novel class. These scores\ncan, in turn, be adopted as input features to machine learn-\ning classifiers for novelty detection. Similarly, contextual\nscores can be calculated simply by inspecting each word\u2019s\nclauses, providing a local perspective for both novel and\nknown classes.\nThe remainder of the paper is organized as follows."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fcf-934e-11ee-ad22-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 1, "chroma:document": "In Section 2, we first summarize related work before we\npresent the details of the TM in Section 3. This forms the\nbasis for our novelty description architecture, covered in\nSection 4. In Section 5, we present our empirical results,\nconcluding the work in the last section.\n2Relatedwork\nSeveral studies have been carried out on supervised\nmulticlass classification in a closed-world setting [ 5]. There\nis a dearth of work addressing open-world settings [ 33],"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fd0-934e-11ee-8e11-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 1, "chroma:document": "Outlier decision boundaryOutlier detection Novelty detection\ntrained example\ndistant\nknown classnovel example\ndecision boundaryAnamoly detection\nAnomaly\nFig.1 Visualization of outlier detection, anomaly detection and novelty detection17466"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fd1-934e-11ee-a16d-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 2, "chroma:document": "Word-levelhumaninterpretable...\nwith distance-based methods being one of the earliest\napproaches [ 28]. These approaches rely on nearest neighbor\nsearch, which introduces scalability issues when dealing\nwith larger datasets. Another class of methods are based on\nsingle-class classifiers. These include One-Class SVM [ 50]\nand SVDD [ 55]. Further, the decision score from SVM has\nbeen used to produce a probability distribution for novelty\ndetection [ 44]. As no negative training samples are used,"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fd2-934e-11ee-ba78-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 2, "chroma:document": "single-class classifiers struggle with maximizing the class\nmargin. To overcome the problem of One-Class SVMs,\na new learning method named center-based similarity\nspace (CBS) was proposed in [ 20], which transforms each\ndocument in a closed boundary to a central similarity vector\nthat can be used in a binary classifier.\nProbabilistic methods have also been utilized for novelty\ndetection [ 43]. In [ 30], a technique to threshold the entropy"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fd3-934e-11ee-9b79-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 2, "chroma:document": "of the estimated class probability distribution is proposed.\nIn that method, choosing the entropy threshold needs prior\nknowledge. Additionally, the class probability distribution\ncan be misleading when novel data points fall far from\nthe decision boundary. In [ 32]a n d[ 46], an active learning\nmodel is proposed to both discover and classify novel\nclasses during training. However, the appearance of novel\ninstances during testing is not considered."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fd4-934e-11ee-9de4-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 2, "chroma:document": "instances during testing is not considered.\nDNNs have recently been used to address the problem\nof novelty detection. In [ 61], a two-class SVM classifier is\nadopted to categorize known and novel classes. An adver-\nsarial sample generation (ASG) framework [ 23]i su s e d\nto generate positive and negative samples. Similarly, [ 37]\nemploys generative adversarial networks (GANs), where\nthe generator produces a mixture of known and novel data."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fd5-934e-11ee-86ed-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 2, "chroma:document": "The generator is trained with so-called feature matching\nloss, and the discriminator performs simultaneous clas-\nsification and novelty detection. In computer vision, the\nproblem of novel image detection is addressed by intro-\nducing the concept of open space risk [ 49]. This is\nachieved by reducing the half-space of a binary SVM\nclassifier with two parallel hyperplanes that bound the\npositive region. Although the binary SVM reduces the pos-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fd6-934e-11ee-a17c-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 2, "chroma:document": "itive region to half-spaces, their open space risk is still\ninfinite. In [ 5], a method called OpenMAX is proposed,\nwhich estimates the probability of an input belonging to\na novel class. In general, the major weaknesses of these\nmethods are high computational complexity and uninter-\npretable inference. A state-of-the-art GAN-based method\nfor unsupervised outlier detection called Single-Objective\nGenerative Adversarial Active Learning (SO-GAAL) and"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fd7-934e-11ee-906d-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 2, "chroma:document": "Multi-Objective Generative Adversarial Active Learning\n(MO-GAAL) was proposed in [ 41]. The method is based\non a min-max game between a generator and a discrimina-\ntor. The training process of the generator is paused before\nconvergence to synthesize outliers, which is subsequently\nused to train the discriminator to recognize the outliers.However, the method is primarily designed for high-dimen-\nsional data, requiring extensive problem-specific hyper-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fd8-934e-11ee-80a5-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 2, "chroma:document": "parameter tweaking. The unsupervised learning method\nCOPOD [ 40] is a more recent approach that is inspired\nby copulas for modeling multivariate data distributions. In\ncomparison to other methods, COPOD is computationally\nefficient, interpretable, and is unaffected by feature dimen-\nsion. However, the method fails to handle complex features\nand intricate nonlinear relations.\nApart from the studies on the document-level novelty\ndetection, novelty detection at the event level arises from"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fd9-934e-11ee-8ada-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 2, "chroma:document": "topic detection, which focuses on the online event and story\ndetection [ 38]. The study at the event level primarily con-\nsists of clustering algorithms that measure the closeness of\nincoming events or stories to one of the clusters depend-\ning on a pre-defined threshold. Novelty detection at the\nsentence level was investigated in Text Retrieval Confer-\nences (TREC) by highlighting sentences that include novel\ninformation given a topic and a list of documents [ 52]."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fda-934e-11ee-9fb4-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 2, "chroma:document": "Based on TREC, many studies have been conducted on\nnovelty detection at sentence level [ 56,63], including term\ntranslations, Principal Component Analysis (PCA) vectors,\nSupport Vector Machine (SVM) classification, named enti-\nties patterns, etc. Likewise, a few approaches have been\nintroduced for learning sentence embeddings, including\nSkipThought [ 36], Conceptual Sentence Embedding [ 58],\nand FastSent [ 31]. However, these approaches on embed-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fdb-934e-11ee-bc33-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 2, "chroma:document": "dings are very dependent on the domain-specific down-\nstream tasks. Recently introduced powerful language mod-\nels, such as ELMo [ 42]a n dB E R T[ 18], have been success-\nful for transfer learning and they are able to learn dynamic\nsentence embedding in an unsupervised manner.\nIn [22], a unified attention architecture is proposed to\ndeal with vector representations of text input in NLP. The\nauthors investigate how information can be retrieved from"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fdc-934e-11ee-8aac-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 2, "chroma:document": "attention in NLP. Further, [ 51] checks whether the attention\nweights provide any interpretability by manipulating the\nweights in pretrained text classification models. They used\nan intermediate representation erasure method to demon-\nstrate that attention weights are unreliable predictors of the\nrelative significance of the specific input. They thus do\nnot accurately explain the model\u2019s decision-making. Addi-\ntionally, [ 53] employed a novel approach for visualizing"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fdd-934e-11ee-abb9-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 2, "chroma:document": "the attention score for each token. This is the first study\non interpretability analysis by visualizing and scoring at\nthe word level. However, as explained in [ 34], the scoring\nacquired using attention methods does not provide a mean-\ningful explanation. A more advanced scoring method known\nas Masked Language Model (MLM) [ 48] uses pretrained\nMLM to score sentences using pseudo-log-likelihood scores\n(PLLs), which involves masking each token one by one."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fde-934e-11ee-bcb3-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 2, "chroma:document": "The method becomes unsuitable for scoring the entire\ntokens of the dataset as the computational complexity rises.17467"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fdf-934e-11ee-aab1-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 3, "chroma:document": "B.Bhattaraietal.\nLikewise, recent keyword extraction (KE) algorithms such\nas YAKE [ 13] and KeyBERT [ 26] are also used to extract\nthe top-scoring tokens from the trained model. To the best\nof our knowledge, in novelty detection, there exists no such\nmethod to measure each word\u2019s contribution to the novelty.\nIn this study, we expand the study on novelty detection with\na method for scoring each word\u2019s contribution to the over-\nall novelty, which offers a clear view to the researchers for"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fe0-934e-11ee-8e09-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 3, "chroma:document": "the reasoning and the interpretation of the results that the\nalgorithm offers.\n3Tsetlinmachine(TM)architecture\nThe TM, proposed in [ 24], is a recent approach to pat-\ntern classification, regression, and novelty detection [ 1,8,\n25]. It captures the frequent patterns of the learning prob-\nlem using conjunctive clauses in propositional logic. Each\nclause is a conjunction of literals, where a literal is a propo-\nsitional/Boolean variable or its negation. Recent research"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fe1-934e-11ee-ba32-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 3, "chroma:document": "reports that the TM performs competitively with state-of-\nthe-art deep learning networks in text classification [ 7,\n47,59,60] along with parallel and asynchronous architec-\nture [ 2] for faster learning across diverse tasks. Further,\ntheoretical studies have uncovered robust convergence prop-\nerties [ 35,62].\nA basic TM accepts a vector X=(x1,...,x o)\u2208{0,1}o\nofoBoolean features as input. For text input, it is typical\nto booleanize the text to form a Boolean set of words, as"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fe2-934e-11ee-b3b9-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 3, "chroma:document": "suggested in [ 7]. The input features, together with their\nnegated counterparts, \u00afx=\u00acx=1\u2212x, form a literal\nsetL={x1,...,x o,\u00acx1,...,\u00acxo}. For classification\nproblems, the sub-patterns associated with the classes are\ncaptured by the TM using mconjunctive clauses C+\njorC\u2212\nj.\nThej=1,...,m / 2 subscript denotes the clause index,\nwhile the superscript indicates the polarity of a clause.\nIn brief, half of the clauses are assigned positive polarity,\ni.e.,C+"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fe3-934e-11ee-a2ac-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 3, "chroma:document": "i.e.,C+\nj, and the other half are assigned negative polarity,\ni.e.,C\u2212\nj. The positive polarity clauses vote for the input\nbelonging to the class favored by the TM, while the negative\npolarity clauses vote against that class, that is, for other\nclasses.\nAc l a u s e C\u03be\nj,\u03be\u2208{ \u2212,+},is formed by ANDing a subset\nL\u03be\nj\u2286Lof the literal set. That is, the set of literals for clause\nC\u03be\njwith polarity \u03becan be written as:\nC\u03be\nj(X)=\u22c0\nl\u2208L\u03be\njl=\u220f\nl\u2208L\u03be\njl.( 1 )"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fe4-934e-11ee-bae7-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 3, "chroma:document": "C\u03be\nj(X)=\u22c0\nl\u2208L\u03be\njl=\u220f\nl\u2208L\u03be\njl.( 1 )\nThe clause evaluates to 1 if and only if all the literals of the\nclause also evaluate to 1. For example, the clause C\u03be\nj(X)=\nx1x2consists of the literals L\u03be\nj={x1,x2}and outputs 1, ifx1=x2=1. The final classification decision is obtained\nby subtracting the negative votes from the positive votes,\nand then thresholding the resulting sum using the unit step\nfunction u:\n\u02c6y=u\u239b\n\u239dm/2\u2211\nj=1C+\nj(X)\u2212m/2\u2211\nj=1C\u2212\nj(X)\u239e\n\u23a0.( 2 )"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fe5-934e-11ee-91d9-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 3, "chroma:document": "\u02c6y=u\u239b\n\u239dm/2\u2211\nj=1C+\nj(X)\u2212m/2\u2211\nj=1C\u2212\nj(X)\u239e\n\u23a0.( 2 )\nFor example, the classifier \u02c6y=u(x 1\u00afx2+\u00afx1x2\u2212x1x2\u2212\u00afx1\u00afx2)\ncaptures the XOR-relation.\nFor learning, the TM employs a team of Tsetlin Automata\n(TA), one TA per literal l\u2208L. Each TA performs one of two\nactions: either include orexclude its designated literal. Each\nclause statistically forwards the feedback to its individual\nTA. The TM employs Type I and Type II feedback. These\nfeedback types control the reward, penalty or inaction"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fe6-934e-11ee-a6b0-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 3, "chroma:document": "received by TAs depending on six factors: (1) target output\n(y=0o ry=1), (2) clause polarity, (3) clause output\n(Cj=0 or 1), (4) literals value ( x=1, or\u00acx=1),\n(5) vote sum, and (6) the current state of the TA. Type I\nfeedback is designed to produce frequent patterns, while\nType II feedback increases the discriminating power of the\npatterns (see [ 25] for details). The feedback guides the\ncomplete system of TAs towards a Nash equilibrium. At any"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fe7-934e-11ee-8b4e-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 3, "chroma:document": "point in the training process, we have mconjunctive clauses\nper class, half of them positive and half of them negative.\nThese can be retrieved and deployed upon completion of\ntraining.\n4Noveltydescription\nBy novelty description, we mean the task of characterizing\nnovel textual content at the word level. For instance, the\nknown content may be reviews of mobile phones, while\nthe novel content could be reviews of grocery stores. For\nthis example, one may define the novel content using words"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fe8-934e-11ee-bd72-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 3, "chroma:document": "associated with grocery stores. However, describing novelty\nat the word level is nontrivial because the meaning of\nwords varies depending on the context they appear in.\nFor example, consider the word \u201cbat\u201d. This word typically\nmanifests in two distinct contexts- it can denote either\n\u201canimal\u201d or \u201csports\u201d. Likewise, the word \u201cbank\u201d can refer to\n\u201criver bank\u201d or \u201ccash bank\u201d. That is, when contextual\nmeaning is considered, the novelty of the word \u201cbat\u201d and"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fe9-934e-11ee-a51b-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 3, "chroma:document": "\u201cbank\u201d can be different based on their respective uses.\nAs a result, measuring and describing novel content is a\nchallenging problem.\nIn general, one can detect and characterize novel content\nby contrasting against the probability of observing textual\ncontent X, given that the content is known. We denote\nthis probability distribution by pknown(X). Assume that\nthe corresponding probability distribution pnovel(X) for17468"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fea-934e-11ee-8073-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 4, "chroma:document": "Word-levelhumaninterpretable...\nnovel content also is available. Then, the optimal novelty\ndetection test for a given false positive rate ( \u03b1) can be\nobtained by thresholding the likelihood ratio pnovel(X) /\npknown(X)[39].\nSince neither pknown(X) orpnovel(X) are available\nto us, we must estimate them using training examples.\nInspired by the work in [ 9] on Semi-Supervised Novelty\nDetection (SSND), we use two sets of examples. One\nset represents known content, while the other represents"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5feb-934e-11ee-b5c1-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 4, "chroma:document": "novel content. We obtain these sets by employing a binary\nclassifier that can distinguish between known and novel\ncontent, such as the one we proposed in [ 8].\n4.1Identifyingnovelwordcandidates\nIn our approach, we begin by training a TM on input\ntexts represented as Boolean bag-of-words, i.e., as word\nsets. A propositional variable represents each word in\nthe vocabulary, capturing the presence/absence of the\ncorresponding word in the input text. We group the texts into"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fec-934e-11ee-8d0b-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 4, "chroma:document": "two classes, Known andNovel . The first represents known\ncontent, and the second represents novel content. Our task\nis to describe how the second group of text is novel at\nthe word level. To this end, we begin by identifying novel\nword candidates, followed by scoring and ranking the words\nbased on their contribution to novelty.\nFigure 2shows our architecture for identifying novel\nword candidates. As seen, upon training, we obtain the\nclauses of the two classes, Known andNovel . We extract all"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fed-934e-11ee-b4cb-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 4, "chroma:document": "the words included in the clauses for each class. Each clause\ncontains a combination of both plain ( PL) and negated\n(NL) words. As such, the plain and the negated words\nserve two different roles. The plain words characterize the\ncorresponding class, while the negated words characterize\nthe other class. We exploit this property as follows, buildingtwo bag-of-words (BOW). The first is a bag of known\nwords, referred to as BK, and the second is a bag of novel\nwords, referred to as BN."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fee-934e-11ee-831e-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 4, "chroma:document": "words, referred to as BN.\nFor class Known , we perform the following procedure:\n\u2013 We consider the words included in positive clauses first.\nHere, the plain words PLare added to the bag of known\nwordsBK, while the negated words are placed in the\nbag of novel words BN.\n\u2013 For negative clauses, we do the opposite. The plain\nwordsPLare added to the novel words bag BN.T h e\nnegated words NL, on the other hand, are added to the\nknown word bag BK.\nThe above procedure is inverted for class Novel :"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fef-934e-11ee-aba2-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 4, "chroma:document": "The above procedure is inverted for class Novel :\n\u2013 For the positive clauses, the plain words PLare added\nto the novel word bag BN, while the negated words are\nadded to the known word bag BK.\n\u2013 Conversely, for the negative clauses, the plain words are\nadded toBK, characterizing the known class, while the\nnegated words NLare added to BN.\n4.2Scoringwordnovelty\nWith the word bags BKandBNavailable, we calculate\nnovelty scores at the word level as follows. From the"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5ff0-934e-11ee-9df2-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 4, "chroma:document": "unique words in the bags BKandBN, we produce\ntwo corresponding word sets, SKandSN. Assume these\nrespectively contain KandNunique words:\nSK={s1,s2,...,s k,...,s K},\nSN={s1,s2,...,s n,...,s N}.( 3 )\nHere, skrepresents a specific word in the set SK, while sn\nrepresents a specific word in the set SN.\nText Literals\nBinary encoded Literals\nPositive\nclausesPositive\nclausesNegative\nclausesNegative\nclausesknown class novel class\nKnown BOW list Novel BOW listNovel BOW list Known BOW list"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5ff1-934e-11ee-8bdc-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 4, "chroma:document": "Fig.2 Tsetlin Machine architecture for generating word sequences17469"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5ff2-934e-11ee-a660-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 5, "chroma:document": "B.Bhattaraietal.\nWe next estimate the occurrence probability psiof each\nwordsiinSK, from the known class. The estimate is based\non the relative frequency of siin the word bag BKas given\nby ( 4):\npK\nsi=FK\ni\u2211K\nk=1FK\nk.( 4 )\nHere,FK\niis the frequency of word siinBK, i.e., the number\nof times that word sihas the appropriate role in one of\nthe clauses (as defined in the previous section). To prevent\ninfinite or zero scores, we assume that every word has a"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5ff3-934e-11ee-a79e-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 5, "chroma:document": "minimum frequency of 1. In the following, we denote the set\nof relative frequencies for the words from BKbypK, while\npNis the set of relative frequencies for the words from BN,\nas captured by ( 5):\npK={pK\ns1,pK\ns2,...,PK\nsK},\npN={pN\ns1,pN\ns2,...,pN\nsN}.( 5 )\nThe calculation of the novelty score for each word depends\non whether si\u2208SK,si\u2208SN, or both, as shown in ( 6):\nScore(si)=\u23a7\n\u23aa\u23aa\u23a8\n\u23aa\u23aa\u23a9pN\nsi\npKsiifsk\u2208SK\u2229SN,\n0i f sk\u2208SK\\SN,\n\u221e ifsk\u2208SN\\SK.(6)\nHere, pN\nsiandpK\nsidenote the estimated occurrence prob-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5ff4-934e-11ee-9104-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 5, "chroma:document": "siandpK\nsidenote the estimated occurrence prob-\nabilities of the word sifrompNandpK, respectively.\nThe score defines how much a word contributes in a sen-\ntence/document to make it novel. That is, a higher score\nsignals higher novelty and vice versa. Figure 3shows the\nresulting TM-based architecture and flow of information for\nthe above scoring approach.Additionally, we also propose a contextual scoring\napproach to capture multiple word meanings determined"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5ff5-934e-11ee-9468-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 5, "chroma:document": "by context. We presume that words that appear in the\nsame clause are related semantically, and accordingly, we\nuse clause co-occurrence of words to measure semantic\nrelations. The intent is to differentiate between, for example,\nthe meaning of \u201capple\u201d in \u201capple phone\u201d and the meaning of\n\u201capple\u201d in \u201capple fruit\u201d. We achieve this through leveraging\nclauses that capture \u201capple\u201d and \u201cphone\u201d in combination\nwith other clauses that capture \u201capple\u201d and \u201cfruit\u201d."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5ff6-934e-11ee-9b11-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 5, "chroma:document": "The scoring is again performed in two steps:\n1. Rather than measuring the frequency of individual\nwords, we now measure frequency of co-occurrence\namong the TM clauses. For instance, let us consider\nthe word pair (s1,s2)and novel class, associated with a\ntotal number of mclauses. The frequency of the word\npair occurring together in the clauses is then given as:\npN\ns1,s2=FN\ns1,s2\nm.( 7 )\nHere,FN\ns1,s2is the number of times the word pair occur\ntogether across the mclauses of the novel class."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5ff7-934e-11ee-a270-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 5, "chroma:document": "together across the mclauses of the novel class.\n2. Finally, the contextual score for the word pair ( s1,s2)i n\nclass Novel can be defined as:\nScoreN\ncontext(s1,s2)=pN\ns1,s2\npNs1\u00d7pNs2.( 8 )\nAbove, pN\ns2andpN\ns1are the individual frequencies of\neach word across the novel clauses, from the previous\nsubsection.\nNotice how the above score increases with lower indi-\nvidual frequencies and higher joint frequency, measuring\nRelative frequency\ncalculator Relative frequency"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5ff8-934e-11ee-b173-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 5, "chroma:document": "Relative frequency\ncalculator Relative frequency\ncalculator Known Class Novel Class\nTMKnown Clauses\nword list Novel Clauses\nword list \nScore Generatorword in word in \nyes yes\nword ScoreScore == very high\n(novel word)Score == very low\n(known word)No No\nwordscore\nFig.3 Novelty scoring calculation for each word17470"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5ff9-934e-11ee-bc78-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 6, "chroma:document": "Word-levelhumaninterpretable...\ndependence over the clauses. In the same way, we can cal-\nculate dependence over the clauses for the known class as\nwell.\n4.3Casestudy\nWe now demonstrate our novelty description approach,\nsteb-by-step, using two example sentences from the sports\ndomain. For illustration purposes, we consider the class\nCricket to be Known and the class Rugby to be Novel .\n\u2013 Class : Cricket (Known)\nTe x t : England won the cricket match by hitting six in\nthe last ball."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5ffa-934e-11ee-a074-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 6, "chroma:document": "the last ball.\nWords :\u201cEngland\u201d ,\u201cwon\u201d ,\u201ccricket\u201d ,\u201cmatch\u201d ,\u201chit\u201d ,\n\u201csix\u201d ,\u201cball\u201d .\n\u2013 Class : Rugby (Novel)\nText : England won the rugby match despite using old ball.\nWords :\u201cEngland\u201d ,\u201cwon\u201d ,\u201crugby\u201d ,\u201cmatch\u201d ,\n\u201cdespite\u201d ,\u201cold\u201d ,\u201cball\u201d .\nWe first create the set of 10 unique words W={\u201cEngland\u201d ,\n\u201cwon\u201d ,\u201ccricket\u201d ,\u201cmatch\u201d ,\u201chit\u201d ,\u201csix\u201d ,\u201cball\u201d ,\u201crugby\u201d ,\n\u201cdespite\u201d ,\u201cold\u201d}from the words in the two sentences,\neach with a unique index o. From this set, we produce the"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5ffb-934e-11ee-904f-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 6, "chroma:document": "input feature vector for the TM, X=[x1,x2,...,x 10].\nEach propositional input xoinXrefers to a particular word.\nJointly, the propositional inputs are used to represent an\ninput text. If a word wo\u2208Wis present in the document, the\ncorresponding propositional input xois set to 1, otherwise,\nit is set to 0.\nAfter TM training, we obtain a set of clauses, as exam-\nplified in Table 1. The clauses (C+\n1)K,(C+\n2)K,(C\u2212\n1)N,\n(C\u2212\n2)Nvote for class Known , while (C\u2212\n1)K,( C\u2212\n2)K,\n(C+\n1)N,( C+"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5ffc-934e-11ee-b89f-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 6, "chroma:document": "1)K,( C\u2212\n2)K,\n(C+\n1)N,( C+\n2)Nvote for class Novel . These clauses are\nthen used to produce two bag-of-words, BKandBN.A l l\nthe plain words in (C+\n1)K,(C+\n2)K,(C\u2212\n1)N,(C\u2212\n2)Nare\nplaced in BK, while all the negated words are placed in\nBN. Since none of the words are negated in the clauses,\nwe now have BK=(\u201cEngland\u201d ,\u201ccricket\u201d ,\u201cmatch\u201d ,\u201chit\u201d ,\n\u201csix\u201d ,\u201ccricket\u201d ,\u201csix\u201d ,\u201ccricket\u201d ,\u201cwon\u201d ,\u201csix\u201d ,\u201cball\u201d ,\n\u201ccricket\u201d ,\u201chit\u201d ,\u201csix\u201d ). Correspondingly, all the plain\nwords in (C\u2212\n1)K,( C\u2212\n2)K,( C+"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5ffd-934e-11ee-8055-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 6, "chroma:document": "words in (C\u2212\n1)K,( C\u2212\n2)K,( C+\n1)N,( C+\n2)Nare placed in\nBN, while all the negated words are placed in BK.Within each bag-of-words, each word occurs with a\ncertain frequency. For instance, the word \u201cmatch\u201d occurs\nonce inBKand twice in BN. Notice that the total number\nof word occurrences are different for each class \u2013 14 words\nin class Known and 13 words in class Novel . Hence, the\nrelative frequency for \u201cmatch\u201d in class Known becomes\npK\nmatch=1\n14=0.071 while for class Novel it becomes\npN"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5ffe-934e-11ee-a073-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 6, "chroma:document": "14=0.071 while for class Novel it becomes\npN\nmatch=2\n13=0.154. Table 2lists the frequencies of the\nwords per class.\nWe are now ready to calculate the novelty score for each\nword in W. Let us consider the word \u201crugby\u201d from the novel\nword set and the word \u201ccricket\u201d from the known word set.\nFor \u201crugby\u201d, we first calculate its relative frequency ( 4). In\nthe bag-of-word BNfor class Novel , \u201crugby\u201d occurs four\ntimes, i.e., FN\nrugby=4 .S i n c ew ea s s u m et h a taw o r dh a s"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e5fff-934e-11ee-bdcc-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 6, "chroma:document": "a minimum frequency of 1, we further have FK\nrugby=1,\ndespite \u201crugby\u201d not appearing in the text from class Known .\nFrom Table 2, we observe that the total word frequencies\nfor the known and novel classes are 14 and 13, respectively.\nHence, the relative frequencies for \u201crugby\u201d becomes\nprugby(K)=0.307 for class Known andprugby(N)=\n0.071 for class Novel (4).\nBecause the clauses characterize each class Known and\nNovel , notice how \u201crugby\u201d gets the relatively high novelty"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6000-934e-11ee-a5df-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 6, "chroma:document": "scoreScore rugby =4.651. That is, its relative frequency\nis high in the novel class and low in the known class.\nConversely, the word \u201ccricket\u201d is repeated four times in\nBKandonce inBN. Its relative frequencies thus becomes\npcricket(K)=0.28 for class Known andpcricket(N)=0.076\nfor class Novel . Accordingly, the novelty score becomes\nScore cricket =0.271, which is a low score denoting a\nstrong inclination of the word towards the known class.\nOverall, Table 2shows how the words characterizing"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6001-934e-11ee-9689-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 6, "chroma:document": "class Known get a relatively low novelty score, while those\ncharacterizing class Novel o b t a i nh i g hs c o r e s .\n5Resultsanddiscussions\nIn this section, we evaluate our proposed novelty descrip-\ntion approach on two publicly available datasets: BBC\nSports andTwenty Newsgroups . The performance of the TM\nframework for novelty detection was previously investigated\nTable 1 Clauses with conjunctive word patterns for known and novel class\nKnown Clauses Novel Clauses\n(C+"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6002-934e-11ee-a016-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 6, "chroma:document": "Known Clauses Novel Clauses\n(C+\n1)K=\u201cEngland\u201d \u2227\u201ccricket\u201d \u2227\u201cmatch\u201d \u2227\u201chit\u201d\u2227\u201csix\u201d (C+\n1)N=\u201cEngland\u201d \u2227\u201cwon\u201d \u2227\u201crugby\u201d \u2227\u201cold\u201d\n(C\u2212\n1)K=\u201cwon\u201d \u2227\u201crugby\u201d \u2227\u201cball\u201d (C\u2212\n1)N=\u201ccricket\u201d \u2227\u201cwon\u201d \u2227\u201csix\u201d\u2227\u201cball\u201d\n(C+\n2)K=\u201ccricket\u201d \u2227\u201csix\u201d (C+\n2)N=\u201crugby\u201d \u2227\u201cmatch\u201d \u2227\u201cdespite\u201d \u2227\u201cold\u201d\n(C\u2212\n2)K=\u201crugby\u201d \u2227\u201cmatch\u201d (C\u2212\n2)N=\u201ccricket\u201d \u2227\u201chit\u201d\u2227\u201csix\u201d17471"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6003-934e-11ee-af1a-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 7, "chroma:document": "B.Bhattaraietal.\nTable 2 Relative frequency and score for each word\nKnown Novel\nWord Frequency Relative frequency Score Word Frequency Relative frequency Score\nEngland 1 0.071 1.070 England 1 0.076 1.070\nWon 1 0.071 2.169 Won 2 0.154 2.169\nCricket 4 0.28 0.271 Rugby 4 0.307 4.651\nMatch 1 0.071 2.169 Match 2 0.154 2.169\nHit 2 0.142 0.535 Despite 1 0.076 1.15\nSix 4 0.28 0.271 Old 2 0.153 2.31\nBall 1 0.071 1.070 Ball 1 0.076 1.070\nin [8] and is summarized in Table 3. Notably, as has"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6004-934e-11ee-a513-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 7, "chroma:document": "been found across several datasets, a one-class SVM on\nthe simple mean embeddings established a strong base-\nline. Here, we further explore our model\u2019s effectiveness\nat producing discriminative novelty scores at the word\nlevel using TM clauses. To obtain robust performance and\nensure that the results are not influenced by the data, we\nperform a one-class classification using leave-one-out eval-\nuation on 20 Newsgroup dataset. This paper deals with"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6005-934e-11ee-8d1a-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 7, "chroma:document": "the post-processing after novelty detection to deal with the\nnovelty scoring at the word level. However, the leave-one-\nout evaluation is necessary because this study leverage the\nperformance of the TM framework in terms of novelty\ndetection. We employ the ROC AUC to quantify the nov-\nelty detection performance by using the ground truth labels\nduring testing. Table 4shows the performance compari-\nson of our method and the baseline algorithms, including"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6006-934e-11ee-8bae-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 7, "chroma:document": "a one-class classifier. In the leave-one-out setup, one of\nthe classes is considered a known class, while the remain-\ning classes are treated as novel. The training is conducted\nusing a known class, whereas testing is carried out on\nsamples from a novel class. The ROC AUC is computed\nduring testing with the assumption that the samples from\nthe known class are labeled as y=0 and from novel\nclass as y=1. Our method outperforms baselines algo-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6007-934e-11ee-a489-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 7, "chroma:document": "rithms in five out of six evaluation setups with a significant\nmargin.\nIn the following, we compare the scoring mechanism of\nour framework with attention and TF-IDF as a baseline.\nTo ensure a fair comparison, the attention score for each\nword is calculated as described in Section 5.1.1 .F o r\nTF-IDF, We calculate TF separately for the known and\nnovel classes. Conversely, IDF is calculated using all the\ndocuments from both classes (to suppress common words"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6008-934e-11ee-911b-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 7, "chroma:document": "such as stop words). Unlike attention and TF-IDF, even if\na word is present in most documents, our scoring considers\nboth relevance and context. For example, if a word from\nclass Novel also is present in class Known , our modelcan nevertheless assign more weight to that word. This\nhappens when a word, while syntactically the same in both\nclasses, acquires a novel meaning in the novel class due\nto its appearance in a novel context. The latter contextual"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6009-934e-11ee-9972-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 7, "chroma:document": "information is captured through those clauses of the novel\nclass that trigger for that word. As such, attention and\nTF-IDF are not context-aware. Moreover, these methods\nprove especially beneficial on more extensive datasets, such\nas 20 Newsgroups and BBC Sports, since they filter out\ngeneral language contexts that are less discriminative for\nthe characterization of a text corpus, making them a strong\nbaseline for performance comparison.\nTo provide a comparison, we plot the cumulative"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e600a-934e-11ee-86ed-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 7, "chroma:document": "To provide a comparison, we plot the cumulative\nfrequency distribution (CFD) for the scores of (1) the words\nonly found in the novel dataset, (2) the words only found\nin the known dataset, and (3) the words shared by both\ndatasets. In brief, the CFD demonstrates that the word scores\ngenerated by the baseline are relatively similar for both\nknown and novel classes. Thus, the baseline methods lack\nthe discriminatory power necessary to distinguish between\nthe two categories of words."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e600b-934e-11ee-972a-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 7, "chroma:document": "the two categories of words.\nTable 3 Performance comparison of TM framework with cluster and\noutlier-based novelty detection algorithms\nAlgorithms 20 Newsgroup BBC sports\nLOF 52.51 % 47.97 %\nFeature Bagging 67.60 % 54.38 %\nHBOS 55.03 % 49.53 %\nIsolation Forest 52.01 % 49.35%\nAverage KNN 76.35 % 55.54 %\nK-Means clustering 81.00 % 47.70 %\nOne-class SVM 83.70 % 83.53 %\nSO-GAAL 80.2% 83.50%\nMO-GAAL 82.9% 86.68%\nCOPOD 84.4% 86.09%\nTM framework 82.50% 89.47%17472"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e600c-934e-11ee-83b6-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 8, "chroma:document": "Word-levelhumaninterpretable...\nTable 4 ROC AUC (%) of one-class classification with leave-one-out evaluation on 20 Newsgroup\nNormal class ABOD CBLOF HBOS IForest KNN LOF OCSVM COPOD TM\ncomp 0.506 0.618 0.625 0.62 0.622 0.62 0.614 0.627 0.55\nrec 0.508 0.481 0.476 0.483 0.479 0.48 0.48 0.476 0.60\nsci 0.50 0.435 0.449 0.454 0.434 0.435 0.433 0.45 0.53\nmisc 0.511 0.533 0.527 0.534 0.54 0.542 0.534 0.532 0.69\npol 0.492 0.452 0.436 0.445 0.451 0.451 0.454 0.435 0.71"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e600d-934e-11ee-be90-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 8, "chroma:document": "rel 0.494 0.449 0.437 0.456 0.472 0.47 0.457 0.443 0.63\n5.1Baseline\n5.1.1Attentionmechanism\nWe utilize the weights from attention\u2019s layers input repre-\nsentation Aof the trained model. The importance of each\ntoken is calculated based on the attention it receives. For\ninstance, if attention to the token c\u2208Ais higher than the\ntoken d\u2208A,t h e n cis assumed to be \u201cmore significant\u201d\nthandto the model\u2019s output. In our work, the scores are\ncalculated using scaled-dot product attention mechanism\n[57]."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e600e-934e-11ee-baff-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 8, "chroma:document": "[57].\nLet us consider an input sequence of length o,X=\n(x1,x2,...,x o),w h e r e xirepresents the ithtoken whose\nrepresentation in the attention layer is hi\u2208Rt. The attention\nscore for the ithtoken is as follows:\n\u03b1i=hi\u00d7V\n\u03b2, (9)\nwhere the parameter \u03b2is the scaling factor, and V\u2208Rt\nis the context vector that can be seen as a fixed query\nrequesting the \u201cmost important token\u201d from input. Either the\nword embedding or the encoder\u2019s output can denote token"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e600f-934e-11ee-b759-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 8, "chroma:document": "representation hi. The attention weight can be expressed as:\nai=exp(a i)\u2211\ni\u2032exp(a i\u2032). (10)\nFinally, the complete input sequence is denoted as:\nh=\u2211\ni(aihi). (11)\nIn our experiment, we retrieve the attention score and\nweights for each token using ( 9)a n d( 10) respectively.\nWe conducted experiments using scaled dot-product\nattention ( DP) and additive attention with varying scaling\nfactors ( \u03b2). The attention scores in our experiments are\ngenerated using a Long short-term memory (LSTM) with"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6010-934e-11ee-911d-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 8, "chroma:document": "DP and an affine transformation layer as the input encoder.\nWe used the Adagrad optimizer [ 19] for gradient descent\nand used dropout as regularization to prevent over-fitting.\nTo eliminate the influence of prior knowledge, we learn all\nparameters from scratch and initialize the pre-trained word\nembeddings with a uniform distribution and dimension d=100. A softmax function is applied over a linear layer for\nobtaining the final classification output. The readers are"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6011-934e-11ee-aa34-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 8, "chroma:document": "referred to [ 53] for a detailed theoretical explanation to\ngenerate the attention scores.\n5.1.2Termfrequency-inversedocumentfrequency(TF-IDF)\nA commonly used method to analyze the importance of\na word is the term frequency-inverse document frequency\n(TF-IDF) [ 45]. TF-IDF weighs each word to statistically\nmeasure the significance of the word in a given document.\nTo this end, TF-IDF consists of two factors: normalized\nterm frequency (TF) and inverse document frequency (IDF)."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6012-934e-11ee-b9d6-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 8, "chroma:document": "TF measures the frequency of the word in the document,\nwhereas IDF measures the uniqueness of the word across\ndocuments:\nTF\u2212IDF s=Fs\nF\u00d7log2|D|\n|Ds|+1. (12)\nHere,Fsis the frequency of the word sin the target\ndocument, Fis the sum of the target document word\nfrequencies, |D|is the total number of documents, and |Ds|\nis the number of documents containing the word s.\n5.1.3Keywordextractionalgorithms\nOur method extracts keywords from known and novel"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6013-934e-11ee-90bd-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 8, "chroma:document": "Our method extracts keywords from known and novel\nclasses based on the novelty scores. As a result, we also\ncompare the significant words obtained by our method\nto those captured by existing keyword extraction (KE)\nalgorithms. To do this, we first separate the text documents\nfrom known and novel classes before passing them to\nthe KE algorithms. Additionally, we present the top\n10 keywords captured by these algorithms. For the KE\nbaselines mentioned below, we use the pkepackage [ 10]:"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6014-934e-11ee-a13e-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 8, "chroma:document": "\u2013 TopicRank [ 12]: This is a graph-based KE method that\ndepends on the extraction of the top-ranked topic.\n\u2013 YAKE [ 13]: A lightweight statistical approach for KE.\n\u2013 MultipartiteRank [ 11]: An unsupervised KE method for\nencoding topical information in a multipartite graph\nstructure.17473"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6015-934e-11ee-aed4-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 9, "chroma:document": "B.Bhattaraietal.\n\u2013 BERT-MMR [ 26]: A KE method that leverages Bidi-\nrectional Encoder Representations from Transformers\n(BERT) embeddings and Maximal Marginal Relevance\n(MMR).\n5.2Evaluationmeasures\nWe use the accuracy, Receiver Operating Characteristics\n(ROC) curve, precision, and recall to evaluate the perfor-\nmance of novelty detection using word scores obtained from\nthe proposed method. In general, accuracy is a well-known\nparameter to measure the effectiveness of novelty detection"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6016-934e-11ee-bd42-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 9, "chroma:document": "models, which indicates the percentage of correct prediction\nby a model in a test set. The accuracy is calculated by:\nAccuracy =TP+TN\nTP+TN+FP+FN, (13)\nwhere TP,TN,FP,FNdenotes the samples that are\ncorrect novel, correct normal, incorrect novel, and incorrect\nnormal respectively. And P, N denotes the total novel and\nnormal samples. The precision is defined by the percentage\nof correctly identified novel samples and is give as:\nPrecision =TP\nTP+FP. (14)"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6017-934e-11ee-beb4-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 9, "chroma:document": "Precision =TP\nTP+FP. (14)\nRecall is the percentage of the real novel samples identified\nand is given as:\nRecall =TP\nTP+FN. (15)\nIn general, the higher the precision and the recall, the\nbetter the algorithm. However, the precision and recall are\nmutually constrained. For example, if only one novel sample\nis detected, the precision is 100%, while the recall is very\nlow. And if all samples detected are novel, the recall will be\n100%, while precision tends to be very low. Therefore, we"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6018-934e-11ee-b737-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 9, "chroma:document": "present precision-recall graph in our evaluation.\nThe ROC is insensitive to the number of novel samples\nand is calculated by plotting all potential choices of the\nTPrate (the portion of novel data ranked among the total\nnovel data) against the FPrate (the portion of normal data\nranked among the total novel data). The ROC curve can\nbe summarized using a single value defined as the area\nunder ROC curve (AUC). The ROC value ranges between 0"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6019-934e-11ee-9b2b-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 9, "chroma:document": "and 1 and is regarded as average of the recall. The perfect\ndetection of all test samples would result in ROC value of\n1, whereas the null detection would result in ROC value of\n0. In general, the greater the ROC AUC value, the better\nthe algorithm. [ 27] established that the ROC AUC value\ncorresponds to the probability of a pair (nov, nor) ,w h e r enov is certain true novel samples and nor is certain true\nnormal samples. The ROC AUC can then be defined by:\nROC AUC =\u23a7\n\u23a8"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e601a-934e-11ee-af98-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 9, "chroma:document": "ROC AUC =\u23a7\n\u23a8\n\u23a91,ifscore(nov) > score(nor),\n0,ifscore(nov) < score(nor),\n1/2,ifscore(nov) =score(nor) .\nTherefore, the ROC AUC has a direct probabilistic\ninterpretation. The AUC can be also be defined as:\nAUC =\u222b1\n0ROC(T) d T, (16)\nwhere Tdenotes a threshold to control novel samples. The\nROC AUC is the most often used evaluation metric for\nnovelty detection that provides a ranking. Therefore, in this\npaper, we compare the evaluation alongside other methods,"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e601b-934e-11ee-8b4f-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 9, "chroma:document": "so it can give different aspects of the performance. To ensure\nfairness, effectiveness and reproducibility of the evaluation\nresults, we use scikit-plot library1to compute ROC and\nprecision-recall graphs.\n5.3BBCsportsdataset\nThe BBC sports dataset comprises 737 documents from\nthe BBC sport website organized in five sports article\ncategories, collected from 2004 to 2005. The resulting\nvocabulary encompasses 4 613 terms. For our experiment,\nwe consider the classes \u201ccricket\u201d and \u201cfootball\u201d to be"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e601c-934e-11ee-8ba9-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 9, "chroma:document": "known and the class \u201crugby\u201d to be novel, thus creating\nan unbalanced dataset. For preprocessing, we perform\ntokenization, stopword removal, and lemmatization. We run\nthe TM for 100 epochs with 10 000 clauses, a voting margin\nTof 50, and a sensitivity sof 25.0.\nWe present overall novelty score statistics for the words\ncaptured by the clauses in Table 5. The table demonstrates\nthat words in the class Novel have distinctively higher\naverage scores than words in the class Known .A l s o ,"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e601d-934e-11ee-a977-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 9, "chroma:document": "notice that the shared words have the highest mean and\nstandard deviation. As analyzed further below, this is the\ncase because the TM will mainly use those words when\nforming the decision boundary between the two classes. As\na result, the shared words will appear in more clauses as\ncharacterizing class features. That is, the clauses will either\nsingle out the words in one class or suppress the words in\nthe other class.\nTo gain further insight into the properties of the novelty"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e601e-934e-11ee-b362-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 9, "chroma:document": "score, we plot the CFD for the scores of the novel, known,\nand shared words in Fig. 4. We further compare these\nCFDs with the corresponding ones obtained using attention\nweights in Fig. 5a n dT F - I D Fi nF i g . 10. As can be observed\n1https://scikit-plot.readthedocs.io/en/stable/Quickstart.html.17474"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e601f-934e-11ee-9381-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 10, "chroma:document": "Word-levelhumaninterpretable...\nTable 5 Overall word statistics for BBC sport dataset\nCategory Total word count Average score Standard deviation\nKnown words 6660 0.74 0.23\nNovel words 1941 1.3125 3.75\nShared words 3135 11.30 316.93\nfrom the plot, our approach produces more distinctive\nnovelty scores than both attention and TF-IDF. The novel\nwords typically produce high scores, while the known words\nproduce low scores. In particular, as shown in Fig. 4a, 85%"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6020-934e-11ee-b478-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 10, "chroma:document": "of the known words output scores lower than 1.0. On the\nother hand, as seen in Fig. 4b, only approximately 45% of\nthe words unique for the novel class have scores below 1.\nThe majority of the uniquely novel words produce scores\ngreater than 1.\nWe plot the TM and attention scores for each token in\nFig. 6b and a, respectively. Due to the large span of the\nTM scores, the y-axis is plotted on a log scale. Nonetheless,\nwe note that the scores are structured in successive layers,"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6021-934e-11ee-80b4-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 10, "chroma:document": "with known scores at the bottom, novel scores at the top,and shared scores in the center. We notice that even the\nattention score demonstrates a small degree of differenti-\nation between known and novel categories. However, the\nvariability of the score is quite low when compared to the\nscore generated by TM as seen in Fig. 7boxplot. Addi-\ntionally, the shared word scores produced by the attention\nmechanism exhibit a high degree of resemblance to known\nword scores."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6022-934e-11ee-b1b6-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 10, "chroma:document": "word scores.\nFinally, we plot the scores for words that are shared\nbetween the known and novel classes in Fig. 4c. As can\nbe observed, the words that are shared produce both high\nand low scores. To cast further light on this observation, we\ninvestigate the words that are shared further in Table 6.W e\nsee that the words captured frequently by novel clauses have\nhigh scores, whereas the words that are frequent in known\nclauses have low scores. Additionally, common words (e.g.,"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6023-934e-11ee-a35b-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 10, "chroma:document": "stopwords), also have low scores. For example, the word\n\u201cRugby\u201d, which is highly characteristic for class Novel ,\nis repeated only 5 times in the clauses representing class\nKnown . For the clauses that represent class Novel ,o nt h e\nother hand, it is repeated 215 times. In other words, the\nFig.4 Cumulative frequency distribution (CFD) graph for word scores in different categories of BBC Sports using TM17475"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6024-934e-11ee-8c2d-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 11, "chroma:document": "B.Bhattaraietal.\nFig.5 Cumulative frequency distribution (CFD) graph for word scores in different categories of BBC Sports using attention weights\nshared words constitute words that are either characteristic\nfor class Known or class Novel . This finding also suggests\nthat the scores can be calculated accurately even if the\nwords are present in both categories. We analyze the mostfrequently used words to obtain an intuition of the overall\ntheme captured by the clauses. We generate such lists by"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6025-934e-11ee-a7b0-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 11, "chroma:document": "counting the top words according to the highest scores\nfrom known and novel classes. Such a list may assist a\nFig.6 Visualization of tokens in known, Novel and Shared categories from BBC Sports17476"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6026-934e-11ee-821d-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 12, "chroma:document": "Word-levelhumaninterpretable...\nFig.7 Boxplot of scores in known, Novel and Shared categories from BBC Sports\nTable 6 Composition of shared words in BBC Sport\nComposition Total word count Average score Standard deviation\nKnown words 10 0.11 0.070\nNovel words 17 1941.13 3919.02\nCommon words 3051 1.03 0.99\nTable 7 Example of top words extracted from KE baselines for the Known class in BBC Sports\nTM TopicRank YAKE MultipartiteRank BERT-MMR\nWord Score Word Score Word Score Word Score Word Score"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6027-934e-11ee-8b96-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 12, "chroma:document": "manchester 0.004 players 0.0065 said 2.65 players 0.0053 kickoff 0.25\nmanager 0.040 ball 0.0050 game 5.16 games 0.0050 fifaasian 0.29\narsenal 0.041 team 0.0050 england 6.48 goal 0.0038 espnstar 0.28\nunited 0.043 goal 0.0043 chelsea 7.01 chelsea 0.0029 fifa 0.24\ncricket 0.046 chelsea 0.0038 players 7.08 team 0.0029 juventus 0.23\nchelsea 0.049 wickets 0.0030 team 7.44 manchester 0.0026 matchwinner 0.22\noneday 0.051 oneday 0.0029 oneday 8.35 arsenal 0.0024 sportsweek 0.34"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6028-934e-11ee-9905-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 12, "chroma:document": "striker 0.074 england 0.0027 united 8.39 ball 0.0023 goalkick 0.20\nbatsman 0.114 arsenal 0.0027 league 8.39 england 0.0022 clubmate 0.12\nbowler 0.133 tests 0.0026 arsenal 9.62 matches 0.0021 autobiography 0.1317477"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6029-934e-11ee-8ce9-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 13, "chroma:document": "B.Bhattaraietal.\nTable 8 Example of top words extracted from KE baselines for the novel class in BBC Sports\nTM TopicRank YAKE MultipartiteRank BERT-MMR\nWord Score Word Score Word Score Word Score Word Score\nrugby 11143.58 nations 0.012 england 0.006 nations 0.0089 nflstyle 0.29\nnations 10000 game 0.011 rugby 0.101 england 0.0082 july 0.05\nireland 468.18 player 0.009 wales 0.112 game 0.0074 wednesday 0.08\nflyhalf 54.79 side 0.007 nations 0.113 wales 0.0071 rugby 0.17"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e602a-934e-11ee-9b0b-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 13, "chroma:document": "lions 38.18 wales 0.007 ireland 0.113 player 0.0064 wordclass 0.06\nscrumhalf 25.09 ireland 0.006 game 0.125 side 0.0048 dropkicking 0.12\nflanker 19.61 years 0.006 coach 0.146 team 0.0038 goalkickers 0.21\nirish 17.55 team 0.005 side 0.182 france 0.0036 sportsweek 0.28\ncentre 17.19 ball 0.004 players 0.206 win 0.0033 tournament 0.13\nsquad 9.28 win 0.003 win 0.220 squad 0.0030 kickoff 0.23"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e602b-934e-11ee-a229-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 13, "chroma:document": "Fig.8 ROC curve and precision-recall of known/novel class classification of BBC Sports using word scores obtained from TM\nFig.9 ROC curve and precision-recall of known/novel class classification of BBC Sports using attention scores17478"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e602c-934e-11ee-8931-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 14, "chroma:document": "Word-levelhumaninterpretable...\nFig.10 Cumulative frequency distribution (CFD) graph for TF-IDF scores in different categories of BBC Sports\nFig.11 ROC curve and precision-recall of known/novel class classification of BBC Sports using TF-IDF scores17479"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e602d-934e-11ee-b5e7-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 15, "chroma:document": "B.Bhattaraietal.\nTable 9 Overall word statistics for 20 Newsgroups dataset\nCategory Total word count Average score Standard deviation\nKnown words 23133 0.99 0.21\nNovel words 6921 1.20 1.04\nShared words 5786 3.04 131.62\nTable 10 Composition of shared words in 20 Newsgroups dataset\nComposition Total word count Average score Standard deviation\nKnown words 9 0.14 0.074\nNovel words 33 640.75 2378.87\nCommon words 5697 1.11 0.58"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e602e-934e-11ee-961f-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 15, "chroma:document": "Common words 5697 1.11 0.58\nFig.12 Cumulative frequency distribution (CFD) graph for word scores in different categories of 20 Newsgroups using TM17480"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e602f-934e-11ee-96ab-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 16, "chroma:document": "Word-levelhumaninterpretable...\nFig.13 Cumulative frequency distribution (CFD) graph for word scores in different categories of 20 Newsgroups using attention weights\nuser in weighting and selecting relevant words in a specific\napplication.\nTables 7and8show an example of a top word list for\neach class, from which we make the following observations.First, our proposed method assigns low scores to words\nbelonging to known classes while assigning comparatively"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6030-934e-11ee-8ee7-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 16, "chroma:document": "high scores to words belonging to the novel class. In\ngeneral, the words that appear in a novel context are\nTable 11 Example of top words extracted from KE baselines for the known class in 20 Newsgroups\nTM TopicRank YAKE MultipartiteRank BERT-MMR\nWord Score Word Score Word Score Word Score Word Score\nprogram 0.103 people 0.074 image 4.60 people 0.054 lawbook 0.44\ngun 0.104 gun 0.041 gun 6.77 guns 0.033 interpolation 0.42\nuse 0.117 article 0.038 people 7.19 article 0.027 literature 0.41"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6031-934e-11ee-b553-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 16, "chroma:document": "write 0.145 fire 0.032 file 7.25 government 0.026 reading 0.41\npublic 0.154 government 0.032 article 7.41 fire 0.022 writing 0.41\nfile 0.175 image 0.027 like 10.1 time 0.019 translation 0.41\nimage 0.187 fbi 0.021 jpeg 13.1 day 0.018 lexidata 0.40\nemail 0.074 weapons 0.019 think 15.6 weapons 0.017 prisoncamp 0.17\npolice 0.358 problem 0.018 time 16.6 fbi 0.016 comparable 0.40\nfbi 0.231 information 0.017 program 17.4 year 0.014 literate 0.4017481"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6032-934e-11ee-8a09-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 17, "chroma:document": "B.Bhattaraietal.\nTable 12 Example of top words extracted from KE baselines for the novel class in 20 Newsgroups\nTM TopicRank YAKE MultipartiteRank BERT-MMR\nWord Score Word Score Word Score Word Score Word Score\nteam 450.66 game 0.082 writes 1.48 game 0.065 baseball 0.38\nbaseball 243.62 team 0.074 game 1.55 team 0.048 reporters 0.33\npitcher 62.26 player 0.055 article 2.29 pitch 0.037 salaries 0.32\nleague 55.88 year 0.053 team 2.30 player 0.037 basketball 0.31"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6033-934e-11ee-bda3-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 17, "chroma:document": "season 40.50 baseball 0.049 last 2.95 baseball 0.034 fittest -0.07\nhit 24.63 pitcher 0.042 baseball 3.41 pitcher 0.026 kickoff 0.15\nplay 22.68 ball 0.037 player 3.44 ball 0.026 nba 0.10\nbatting 22.25 runs 0.030 time 3.94 runs 0.022 secretive 0.30\npitching 22.00 season 0.030 hit 4.06 season 0.020 jerseys 0.30\nplayer 21.86 braves 0.030 run 4.66 braves 0.019 catchers 0.30\nboosted. Second, the words that are most representative\nfor the respective classes are captured frequently by"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6034-934e-11ee-9771-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 17, "chroma:document": "clauses, making them the most repeated ones. Third, the\nkeywords captured by other KE baselines are comparable\nto those extracted by our method and accurately define the\ncorresponding classes. We observe that TopicRank, YAKE,\nand MultipartiteRank all yield words with a high degree\nof similarity to our approach. Additionally, we notice that\nBERT-MMR exhibits the worst performance. This might\nbe due to the fact that we utilized pre-trained sentence"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6035-934e-11ee-8ff7-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 17, "chroma:document": "embedding for BERT, and the keywords are extracted from\noverall documents. Even though the words are not highly\nrelevant to the classes, BERT is capable of producing words\nrelating to the class\u2019s general theme. For example, sports-\nrelated words are included in both classes.\nWe now investigate the degree of discrimination power\nour novelty scoring provides, and therefore uniquely"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6036-934e-11ee-b5dc-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 17, "chroma:document": "describes novelty at the word level. To this end, we employlogistic regression for classifying novel text based on the\nword scores obtained from our method. The ROC and\nprecision-recall curves of the experiment are depicted in\nFig. 8for our novelty scoring mechanism. Our method\nprovides the competitive ROC value due to its ability to\ndiscriminate novel samples based on their scores. This capa-\nbility enables our method to acquire a higher true positive"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6037-934e-11ee-a4b7-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 17, "chroma:document": "TPrate since it makes separate analysis of both correct\nnovel, i.e., true positive TPand correct normal, i.e., true\nnegative TN. Figures 9a,10,a n d 11contains corresponding\ncurves when TF-IDF and attention scores are used instead.\nWe see that the classification performance for our nov-\nelty scores is substantially better than what is obtained\nwith TF-IDF. Attention score outperforms our approach for\nBBC Sports dataset by a small percentage. However, our"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6038-934e-11ee-925d-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 17, "chroma:document": "approach outperforms attention in 20 Newsgroups. This can\nbe attributed to the capability of our approach to deal with a\nbig dataset.\nFig.14 Visualization of tokens in known, Novel and Shared categories from 20Newsgroups17482"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6039-934e-11ee-899e-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 18, "chroma:document": "Word-levelhumaninterpretable...\nFig.15 Boxplot of scores in known, Novel and Shared categories from 20Newsgroups\nFig.16 ROC curve and precision-recall of known/novel class classification of 20 Newsgroups using word scores obtained from TM\nFig.17 ROC curve and precision-recall of known/novel class classification of 20 Newsgroups using attention scores17483"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e603a-934e-11ee-a8e1-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 19, "chroma:document": "B.Bhattaraietal.\nFig.18 Cumulative frequency distribution (CFD) graph for TF-IDF scores in different categories of 20 Newsgroups\nFig.19 ROC curve and precision-recall of known/novel class classification of 20 Newsgroups using TF-IDF scores17484"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e603b-934e-11ee-aaa2-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 20, "chroma:document": "Word-levelhumaninterpretable...\nTable13 Co-occurrence matrix showing the information gain between\nwords in BBC Sports\nManchester Chelsea Particular Rugby Flyhalf\nManchester 14363.688 6.324 4.738 0.33 0.848\nChelsea 6.324 19801.49 6.18 0.466 1.326\nParticular 4.738 6.18 30863.006 2.52 4.968\nRugby 0.33 0.466 2.52 486.758 3.952\nFlyhalf 0.848 1.326 4.968 3.952 8888.888\n5.420newsgroupsdataset\nThe 20 Newsgroups dataset contains a total of 18 828\ndocuments partitioned equally into 20 separate classes. In"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e603c-934e-11ee-8700-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 20, "chroma:document": "our experiments, we treat the two classes \u201ccomp.graphics\u201d\nand \u201ctalk.politics.guns\u201d as Known topics, and then use the\nclass \u201crec.sport.baseball\u201d to represent a Novel topic. Again,\nwe train a TM to produce our clause-based novelty scores.\nThe overall statistics of the resulting word scores are shown\nin Tables 9and10, where we observe similar behavior to\nthat observed with the BBC Sports dataset.\nThe CFD plot in Fig. 12presents the score distribution"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e603d-934e-11ee-b0c8-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 20, "chroma:document": "among words per group (known, novel, shared). For known\nwords, in Fig. 12a, we find that 90% of the scores of the\nwords are below around 1.3. In Fig. 12b, however, only 45%\nof the novel word scores fall below approx. 1.3. From the\nplots, it is evident that the majority of the novel words have\nconsiderably higher scores than the known words. Note that\nsome of the novel word\u2019s low scores are attributable to the\npresence of common words (e.g., stop words) in the novel"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e603e-934e-11ee-af3e-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 20, "chroma:document": "bag-of-words. Since the common words, as such, do not\nsignify novelty, the TM clauses do not frequently capture\nthem. As a result, they receive relatively low scores despite\ntheir appearance among the novel documents.\nThe CFD plot for attention and TF-IDF both exhibit\nsimilar behaviour to that of BBC Sports, as seen in Fig. 13\nand 18, respectively. Finally, we again observe that the\nclauses have used the shared words for discrimination (cf."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e603f-934e-11ee-abea-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 20, "chroma:document": "Table 10), resulting in a mix of low and high novelty scores,\na ss h o w ni nF i g . 12c.\nTable14 Co-occurrence matrix showing the information gain between\nwords in 20 Newsgroup\nGuns Weapon Gather Baseball Player\nGuns 12302.96 17.648 15.754 4.036 4.268\nWeapon 17.648 13888.888 12.108 4.66 5.102\nGather 15.754 12.108 14610.272 11.854 15.408\nBaseball 4.036 4.66 11.854 4003.824 18.566\nPlayer 4.268 5.102 15.408 18.566 9255.402Table15 Co-occurrence matrix showing the similarity between words"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6040-934e-11ee-bef9-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 20, "chroma:document": "in BBC Sports using Word2Vec\nManchester Chelsea Particular Rugby Flyhalf\nManchester 1 0.782 0.653 0.598 0.718\nChelsea 0.782 1 0.891 0.820 0.829\nParticular 0.653 0.891 1 0.821 0.941\nRugby 0.598 0.820 0.821 1 0.706\nFlyhalf 0.718 0.829 0.941 0.706 1\nTable 11and Table 12provide examples of the highest-\nscoring words captured by KE baselines, including TM, for\nboth classes. The visualization of the scores are presented\nin Figs. 14and15. Again, we observe a similar behavior as"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6041-934e-11ee-849c-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 20, "chroma:document": "for the BBC Sports dataset. The ROC and precision-recall\ncurves for our novelty scoring mechanism are illustrated in\nFigs. 16a,17a,18,a n d 19a include corresponding graphs\nwhen TF-IDF and attention scores are used instead. Our\nmethod outperforms the ROC value obtained from attention\nbecause of its ability to identify more number of correct\nnovel samples, i.e., true positives TP. However, the TF-IDF\nsurprisingly outperforms both of the methods because of its"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6042-934e-11ee-9982-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 20, "chroma:document": "straightforward scoring system and the dataset\u2019s moderate\nsize. We can see that our scoring approach outperforms the\nbaselines by a wide margin.\n5.5Contextualscoring\nWe also implement a context-based scoring approach to\ninvestigate how multiple words interact to capture novelty.\nAs detailed in Section 4, we compute the combined novelty\nscore by measuring word co-occurrence in clauses. That\nis, we intend to demonstrate how context can help uncover"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6043-934e-11ee-be58-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 20, "chroma:document": "novelty when words have multiple meanings. The context-\nbased scoring is critical since the context can transform\nthe word from being novel to known, such as the meaning\nof the word \u201capple\u201d in \u201capple fruit\u201d and \u201capple phone\u201d.\nFor demonstration, we calculate our proposed context-\nbased novelty score for five words (i.e., two known, two\nnovel, and one common word) in both datasets. For the\nBBC Sports dataset, the pairwise co-occurrence scores"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6044-934e-11ee-b1a0-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 20, "chroma:document": "Table16 Co-occurrence matrix showing the similarity between words\nin 20 Newsgroup using Word2Vec\nGuns Weapon Gather Baseball Player\nGuns 1 0.709 0.726 0.673 0.701\nWeapon 0.709 1 0.648 0.454 0.539\nGather 0.726 0.648 1 0.631 0.686\nBaseball 0.673 0.454 0.631 1 0.764\nPlayer 0.701 0.539 0.686 0.764 117485"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6045-934e-11ee-bada-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 21, "chroma:document": "B.Bhattaraietal.\nare presented in Table 13. We see a significant degree\nof correspondence between words such as \u201cManchester\u201d\nand \u201cChelsea\u201d from class Known . Similarly, there is a\nhigh correspondence between words such as \u201cRugby\u201d and\n\u201cFlyhalf\u201d from class Novel . The common word \u201cParticular\u201d,\non the other hand, shows similar correspondence with words\nfrom both of the classes. Similarly, for the 20 Newsgroups\ndataset, the co-occurrence scores for five words selected"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6046-934e-11ee-a53f-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 21, "chroma:document": "from the known, novel, and common word types are shown\nin Table 14. The words \u201cGuns\u201d and \u201cWeapon\u201d are from class\nKnown and manifest strong co-occurrence. Additionally the\nwords \u201cBaseball\u201d and \u201cPlayer\u201d from class Novel correspond\nstrongly as well. The common word \u201cGather\u201d, on the other\nhand, co-occurs within both of the classes. These examples\ndemonstrate that the words that are most likely to appear in\nthe same context have a high co-occurrence score. This can"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6047-934e-11ee-9aff-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 21, "chroma:document": "be explained by the fact that many clauses capture words\nthat frequently occur together in a similar context.\nWe compare the contextual scores obtained from our\nmethod with the Word2Vec similarity score. To do this, we\nutilize Gensim library to train custom Word2Vec on both\ndatasets. Gensim library enabled us to create word embed-\ndings by training own Word2Vec models on a custom corpus\nusing either CBOW or skip-grams algorithms. Parameter-\nwise, we used an embedding size of 200 and a win-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6048-934e-11ee-ae4b-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 21, "chroma:document": "wise, we used an embedding size of 200 and a win-\ndow size of 5. We compute the cosine similarity between\nwords by using their word vectors (embeddings). The\nfindings are included in Tables 15and 16. We notice\na significant degree of resemblance between the correspond-\ning words from the known and the novel classes. However,\nunlike our method, the similarity scores are less distinct, and\nthe common words are not discernible score-wise.\n6Conclusion"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6049-934e-11ee-b78d-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 21, "chroma:document": "6Conclusion\nIn this work, we propose a Tsetlin Machine (TM)-based\nsolution for word-level novelty description. First, we\nemploy the clauses from a trained TM to capture how\nthe most significant words differentiate a group of novel\ndocuments apart from a group of known documents. Then,\nwe calculate the score for each word based on the role\nit plays in the clauses. The analysis of our empirical\nresults for BBC Sports and 20 Newsgroups demonstrate"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e604a-934e-11ee-a98e-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 21, "chroma:document": "significantly better novelty discrimination power when\ncompared to using attention and TF-IDF. Our empirical\nresults also show that we can capture word relations\nthrough a contextual scoring mechanism that measures co-\noccurrence within TM clauses. By capturing non-linear\nrelationships among words, we can enhance the capability\nof measuring novelty at the word level. However, training\na TM is computationally more expensive than calculating"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e604b-934e-11ee-8e86-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 21, "chroma:document": "TF-IDF, particularly for large datasets with an extensivevocabulary. We will address computation speed in our future\nwork, employing indexing mechanisms and exploiting\nfeature space sparsity.\nFunding Open access funding provided by University of Agder.\nDeclarations\nCon\ufb02ictofInterests The authors declare that they have no conflict of\ninterest.\nOpen Access This article is licensed under a Creative Commons\nAttribution 4.0 International License, which permits use, sharing,"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e604c-934e-11ee-8fa5-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 21, "chroma:document": "adaptation, distribution and reproduction in any medium or format, as\nlong as you give appropriate credit to the original author(s) and the\nsource, provide a link to the Creative Commons licence, and indicate\nif changes were made. The images or other third party material in this\narticle are included in the article\u2019s Creative Commons licence, unless\nindicated otherwise in a credit line to the material. If material is not\nincluded in the article\u2019s Creative Commons licence and your intended"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e604d-934e-11ee-b57c-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 21, "chroma:document": "use is not permitted by statutory regulation or exceeds the permitted\nuse, you will need to obtain permission directly from the copyright\nholder. To view a copy of this licence, visit http://creativecommons.\norg/licenses/by/4.0/ .\nReferences\n1. Abeyrathna KD, Granmo O, Jiao L, Goodwin M (2019)\nThe regression tsetlin machine: A tsetlin machine for con-\ntinuous output problems. In: Oliveira PM, Novais P, Reis\nLP (eds) Progress in Artificial Intelligence, 19th EPIA Con-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e604e-934e-11ee-a0b9-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 21, "chroma:document": "ference on Artificial Intelligence, EPIA 2019, Vila Real, Por-\ntugal, September 3-6, 2019, Proceedings, Part II, Springer,\nLecture Notes in Computer Science, vol 11805, pp 268\u2013280.\nhttps://doi.org/10.1007/978-3-030-30244-3 23\n2. Abeyrathna KD, Bhattarai B, Goodwin M, Gorji SR, Granmo\nO, Jiao L, Saha R, Yadav RK (2021) Massively parallel and\nasynchronous tsetlin machine architecture supporting almost\nconstant-time scaling. In: Meila M, Zhang T (eds) Proceedings"}


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encoding: FLOAT32
metadata:
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{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 21, "chroma:document": "June 27-30, 2016, IEEE Computer Society, pp 1563\u20131572.\nhttps://doi.org/10.1109/CVPR.2016.173\n6. Bentivogli L, Clark P, Dagan I, Giampiccolo D (2011)\nThe seventh PASCAL recognizing textual entailment chal-\nlenge. In: Proceedings of the Fourth Text Analysis Confer-\nence, TAC 2011, Gaithersburg, Maryland, USA, November 14-\n15, 2011, NIST. https://tac.nist.gov/publications/2011/additional.\npapers/RTE7 overview.proceedings.pdf17486"}


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id: 152e6052-934e-11ee-807b-ac198e74a6f7
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{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 22, "chroma:document": "International Conference on Computational Linguistics, Proceed-\nings of the Conference System Demonstrations, December 11-16,\n2016, Osaka, Japan, ACL, pp 69\u201373. https://aclanthology.org/\nC16-2015/\n11. Boudin F (2018) Unsupervised keyphrase extraction with mul-\ntipartite graphs. In: Walker MA, Ji H, Stent A (eds) Proceed-\nings of the 2018 Conference of the North American Chapter of\nthe Association for Computational Linguistics: Human Language"}


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topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
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{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 22, "chroma:document": "tations, ICLR 2017, Toulon, France, April 24-26, 2017, Confer-\nence Track Proceedings, OpenReview.net. https://openreview.net/\nforum?id=Hkg4TI9xl\n31. Hill F, Cho K, Korhonen A (2016) Learning distributed represen-\ntations of sentences from unlabelled data. In: Knight K, Nenkova\nA, Rambow O (eds) NAACL HLT 2016, The 2016 conference of\nthe north american chapter of the association for computational\nlinguistics: Human language technologies, san diego california,"}


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topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6063-934e-11ee-bf95-ac198e74a6f7
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{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 23, "chroma:document": "ECCV 2014 - 13th European Conference, Zurich, Switzer-\nland, September 6-12, 2014, Proceedings, Part III, Springer,\nLecture Notes in Computer Science, vol 8691, pp 393\u2013409.\nhttps://doi.org/10.1007/978-3-319-10578-9 26\n34. Jain S, Wallace BC (2019) Attention is not explanation. In:\nBurstein J, Doran C, Solorio T (eds) Proceedings of the 2019\nConference of the North American Chapter of the Association\nfor Computational Linguistics: Human Language Technologies,"}


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{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 23, "chroma:document": "NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019,\nvol 1 (Long and Short Papers). Association for Computational\nLinguistics, pp 3543\u20133556. https://doi.org/10.18653/v1/n19-1357\n35. Jiao L, Zhang X, Granmo O, Abeyrathna KD (2021) On\nthe convergence of tsetlin machines for the XOR operator.\narXiv: 2101.02547\n36. Kiros R, Zhu Y, Salakhutdinov R, Zemel RS, Urtasun R,\nTorralba A, Fidler S (2015) Skip-thought vectors. In: Cortes\nC, Lawrence ND, Lee DD, Sugiyama M, Garnett R (eds)"}


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id: 152e606a-934e-11ee-8230-ac198e74a6f7
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id: 152e606b-934e-11ee-b19a-ac198e74a6f7
encoding: FLOAT32
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id: 152e606c-934e-11ee-a97f-ac198e74a6f7
encoding: FLOAT32
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id: 152e606d-934e-11ee-bbc0-ac198e74a6f7
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id: 152e606e-934e-11ee-9d9f-ac198e74a6f7
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{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 23, "chroma:document": "Conference and Conference on Empirical Methods in Natural\nLanguage Processing, Proceedings of the Conference, 6-8 October\n2005, Vancouver, British Columbia, Canada, The Association for\nComputational Linguistics, pp 105\u2013112. https://aclanthology.org/\nH05-1014/\n53. Sun X, Lu W (2020) Understanding attention for text classifi-\ncation. In: Jurafsky D, Chai J, Schluter N, Tetreault JR (eds)\nProceedings of the 58th Annual Meeting of the Association"}


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id: 152e606f-934e-11ee-addb-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 23, "chroma:document": "for Computational Linguistics, ACL 2020, Online, July 5-10,\n2020, Association for Computational Linguistics, pp 3418\u20133428.\nhttps://doi.org/10.18653/v1/2020.acl-main.312\n54. Tax DMJ, Duin RPW (1998) Outlier detection using classi-\nfier instability. In: Amin A, Dori D, Pudil P, Freeman H (eds)\nAdvances in pattern recognition, joint IAPR international work-\nshops SSPR \u201998 and SPR \u201998, sydney, NSW, Australia, August 11-\n13, 1998 (Proceedings, Springer), Lecture Notes in Computer Sci-"}


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topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6070-934e-11ee-b893-ac198e74a6f7
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{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 24, "chroma:document": "adversarial sample generation. In: Sierra C (ed) Proceedings of the\nTwenty-Sixth International Joint Conference on Artificial Intelli-\ngence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017,\nijcai.org, pp 3357\u20133363. https://doi.org/10.24963/ijcai.2017/469\n62. Zhang X, Jiao L, Granmo O, Goodwin M (5555) On the\nconvergence of tsetlin machines for the identity- and not operators.\nIEEE Transactions on Pattern Analysis & Machine Intelligence pp\n1\u20131. https://doi.org/10.1109/TPAMI.2021.3085591"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6076-934e-11ee-b729-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 24, "chroma:document": "1\u20131. https://doi.org/10.1109/TPAMI.2021.3085591\n63. Zhang Y, Tsai FS (2009) Combining named entities and tags for\nnovel sentence detection. In: Proceedings of the WSDM\u201909 Work-\nshop on Exploiting Semantic Annotations in Information Retrieval,\nAssociation for Computing Machinery, New York, NY , USA,\nESAIR \u201909, p 30\u201334. https://doi.org/10.1145/1506250. 1506256.\nPublisher\u2019s note Springer Nature remains neutral with regard to\njurisdictional claims in published maps and institutional affiliations."}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6077-934e-11ee-8a34-ac198e74a6f7
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{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 24, "chroma:document": "Bimal Bhattarai received\nthe B.Tech. degree in elec-\ntronics and communication\nengineering from JNTUA,\nIndia, in 2015, and the M.Sc.\ndegree from the Department\nof Information and Com-\nmunication Engineering,\nChosun University, South\nKorea. He is currently a PhD\nScholar with the Department\nof Information and Commu-\nnication Technology, UiA.\nHis research interests include\ndata science, natural language\nprocessing, tsetlin machine,\nindoor positioning and navigation, machine learning, and deep"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6078-934e-11ee-870f-ac198e74a6f7
encoding: FLOAT32
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{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 24, "chroma:document": "learning applications.\nOle-Christo\ufb00er Granmo is\nthe Founding Director of Cen-\ntre for Artificial Intelligence\nResearch (CAIR), Univer-\nsity of Agder, Norway. He\nobtained his master\u2019s degree\nin 1999 and the PhD degree in\n2004, both from the University\nof Oslo, Norway. Dr. Granmo\nhas authored in excess of\n150 refereed papers with 6\nbest paper awards within\nmachine learning, encompass-\ning learning automata, bandit\nalgorithms, Tsetlin machines,\nBayesian reasoning, rein-"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e6079-934e-11ee-8e15-ac198e74a6f7
encoding: FLOAT32
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{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 24, "chroma:document": "Bayesian reasoning, rein-\nforcement learning, and computational linguistics. He has further\ncoordinated 7+ research projects and graduated 55+ master- and 7\nPhD students. Dr. Granmo is also a co-founder of the Norwegian\nArtificial Intelligence Consortium (NORA). Apart from his academic\nendeavours, he co-founded the company Anzyz Technologies AS.\nLei Jiao received the B.E.\ndegree in telecommunications\nengineering from Hunan Uni-\nversity, Changsha, China, in\n2005, the M.E. degree in"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e607a-934e-11ee-a693-ac198e74a6f7
encoding: FLOAT32
metadata:
{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 24, "chroma:document": "2005, the M.E. degree in\ncommunication and informa-\ntion system from Shandong\nU n i v e r s i t y ,J i n a n ,C h i n a ,i n\n2008, and the Ph.D. degree in\ninformation and communica-\ntion technology from the Uni-\nversity of Agder (UiA), Nor-\nway, in 2012. He is currently\nw o r k i n ga sa nA s s o c i a t eP r o -\nfessor with the Department of\nInformation and Communica-\ntion Technology, UiA. His research interests include reinforcement"}


created_at: 2023-12-05 09:10:18
topic: persistent://default/default/a0c119a8-dc6b-46d3-8158-5f41a08fd57f
id: 152e607b-934e-11ee-91dd-ac198e74a6f7
encoding: FLOAT32
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{"source": "source_documents\\s10489-022-03281-1.pdf", "page": 24, "chroma:document": "learning, Tsetlin machine, resource allocation and performance evalu-\nation for communication and energy systems.17489"}


