In this paper , it is also shown that the roughness in contours is a suitable characteristic feature for diagnosis of ill-defined malignant tumors with AUC equal to 0.94 in their classification . The extracted information was also found to be useful in the classification of early malignancies whereas in the classification of spiculated and ill-defined malignant masses in their early stage from those of benign tumors , we achieved high accuracy of 0.99 and 0.90 for AUC , respectively . For all but the most problematic datasets , the algorithm also produced very good estimates of AUC standard error . The AUC estimates compared well with Wilcoxon estimates for continuously distributed data and are expected to be superior for categorical data . CONCLUSION This implementation of the PBM is reliable in a wide variety of ROC curve-fitting tasks . TAN structures . Resulting models validity was assessed with in-sample AUC and stratified cross-validation , also comparing with previously published model . Overall , models achieved good discriminative power ( AUC>70 % ) and validity ( measures consistently above 70 % ) . Sometime , the statistician or pharmacokineticist has to choose one particular method to calculate AUC depending on the actual concentration data . Among many pharmacokinetic approaches and modeling analyses , traditional trapezoidal , log-linear trapezoidal , Lagrange polynomial integration , and Purves method are commonly used in calculation of AUC . This paper will discuss the four methods of AUC calculation in pharmacokinetic studies of drug . ABSTRACT The answer-until-correct ( AUC ) method of multiple-choice ( MC ) testing involves test respondents making selections until the keyed answer is identified . Despite attendant benefits that include improved learning , broad student adoption , and facile administration of partial credit , the use of AUC methods for classroom testing has been extremely limited . This study presents scoring properties and item analysis for 26 AUC university course examinations , administered using a commercial scratch-card response system . Both classical logistics regression and AUC optimized method perform well in improving the model , but logistic regression still better in some aspects . AUC Optimized model has higher AUC than logistic regression model but has lower Kolmogorov-Smirnov Score . Theorizing about treatment response was then done by evaluating the tree performance . Results The classification tree model has an area under the curve ( AUC ) classification performance of 67 % corresponding to a sensitivity of 0.69 and a specificity of 0.51 . This result in predicting patient-level response was slightly better than the logistic regression model featuring an AUC of 64 % ( 0.63 sensitivity and 0.54 specificity ) . To this end , we introduce the new concepts of strict ranking representability , a restrictive condition that can be verified easily , and AUC ranking representability , a practically more useful condition that is more difficult to verify . However , the link between pairwise bipartite rankings and dice games allows us to formulate necessary transitivity conditions for AUC ranking representability . A sufficient condition on the other hand is obtained by introducing a new type of transitivity that can be verified by solving an integer quadratic program . The features selected by FOS were used in a FOS predictor and nearest-neighbour predictor and had an area under the receiver operating curve ( AUC ) of 0.889 and 0.791 respectively . The FOS predictor AUC is significantly improved over the signal enhancement ratio predictor with an AUC of 0.706 ( p = 0.0035 for the difference in the AUCs ) . Moreover , using FOS-selected features in a support vector machine increased the AUC over that resulting when the features were manually selected . We model the anomaly score function by a neural network-based unsupervised anomaly detection method , e.g. , autoencoders . The proposed method performs well even when only a small number of inexact labels are available by incorporating an unsupervised anomaly detection mechanism with inexact AUC maximization . Using various datasets , we experimentally demonstrate that our proposed method improves the anomaly detection performance with inexact anomaly labels , and outperforms existing unsupervised and supervised anomaly detection and multiple instance learning methods . We now compare our method against the results in . In this paper they introduce a new measure of performance called AUC - 100 , which is something similar to normalized cumulative rewards up to 20 million frames . Table [ reference ] displays the results for our reference DQN and bootstrapped DQN as Boot - DQN . We then describe one of the most powerful and widely used classifiers for high dimensional feature spaces ; the support vector machines ( SVM ) . We cover the area of classifier evaluation and comparison to provide practitioners with essential understanding of how to test , validate and select the appropriate models for their applications . Finally , we summarize the main advances in the field of ensemble learning , feature subset selection and feature subset ensembles . In fact , even when Overall Accuracy is the classification metric , it is not the best metric to use within the wrapper . In addition , we find that there is no direct connection between the metric inside the wrapper and used for classification evaluation : the metrics show similar patterns across all four balance-aware metrics ( e.g. , all but Overall Accuracy ) . We examine several variations to a TF*IDF-based approach for this task , empirically analyze their effects , and evaluate our system on a large collection of images from current news newsgroups . In addition , we investigate alternative classification and evaluation methods , and the effects that secondary features have on indoor/outdoor classification . Using density estimation over the raw TF*IDF values , we obtain a classification accuracy of 82 % , a number that outperforms baseline estimates and earlier , image-based approaches , at least in the domain of news articles , and that nears the accuracy of humans who perform the same task with access to comparable information . To further advance the automatic process of land use/cover ( LULC ) classification extraction through remote sensing ( RS ) images , by reading many literatures , we summarized the problems , research difficulties and development trends in the process of information extraction and classification of land use . Overall , LULC Classification and extraction based on RS images include 3 tasks : data source selection , sampling design , classification method selection and classifier performance evaluation . These tasks are all important , that is , interdependence and mutual influence . 7.1 Introduction . 7.2 Classifying evaluation . 7.3 ' Quick And Dirty ' . Moreover , the implication of evaluation depends on such criteria as evaluation needs , evaluation purpose , typically applied methods , the role of evaluator and others . The paper also provides a discussion on the evaluation in historical perspective and the classification of evaluation models . The article identifies and describes the main evaluation models such as politically controlled studies , objectives orientated evaluations , management information systems , decision orientated evaluations , etc . Though good results have been achieved in the first round of undergraduate teaching evaluation , there are still a few problems with the assessment . A new round of evaluation should be tailored to the current situation and emphasis should be laid in the process on legalization of higher education evaluation , diversification of evaluation subjects and indexes , classification of the evaluation , standardization of the organization , regularization of the specialized evaluation , improvement of the accuracy of targeting in the evaluation , rectification of the thoughts and ideas of education , revision and adjustment of the evaluation criterion system so as to formulate a long-term higher education quality guaranteeing system . Specifically , we conduct experiments on the widely - used fine - grained benchmark Caltech - UCSD birds dataset ( CUB200 - 2011 ) . The classification task is to discriminate among 200 species of birds , and is challenging for computer vision systems due to the high degree of similarity between categories . It contains 11 , 788 images of 200 bird species . Doing this allows the network to retain spatial information and learn the parameters of these layers as in other convolutional layers . Lastly , neurons in the last layer ( i.e. , the classification layer ) are grouped into feature maps , where denotes the number of classes . The output of the classification layer is then converted into normalized probability values via a softmax function . section : Relational Learning Relational classification ( or collective classification ) methods [ reference ][ reference ][ reference ][ reference ] use links between data items as part of the classification process . Exact inference in the collective classification problem is NP - hard , and solutions have focused on the use of approximate inference algorithm which may not be guaranteed to converge [ reference ] . x0jj2Þ ð7Þ When classifying , Support Vector Machines separate the different classes of data by a hyper - plane contained by the decision function in Eq . ( 8 ) . If not specified , all models are trained with data augmentation : randomly performs horizontal flips , and takes a random crop with 32 32 for CIFAR - 10 and CIFAR - 100 ( 28 28 for Fashion - MNIST ) from images padded by 4 pixels on each side . subsubsection : Classification Evaluation Classification accuracy on different datasets . subsection : Evaluating DCGANs capability to capture data distributions We propose to apply standard classification metrics to a conditional version of our model , evaluating the conditional distributions learned . We trained a DCGAN on MNIST ( splitting off a 10 K validation set ) as well as a permutation invariant GAN baseline and evaluated the models using a nearest neighbor classifier comparing real data to a set of generated conditional samples . Note that we only need to learn the parameters in the localisation network . One of the key contribution of STN is making the whole process differentiable , so that the localisation network can be directly optimized for the classification objective via back propagation . Please refer to for more technical details . use boundary detection and hierarchical grouping before performing category segmentation . The common attribute in all these approaches is the use of hand engineered features for classification of either RGB or RGBD images . The success of deep convolutional neural networks for object classification has more recently led researchers to exploit their feature learning capabilities for structured prediction problems such as segmentation . Because the feature map dimension increases linearly in the case of additive PyramidNets , the feature map dimensions of the input - side layers tend to be larger , and those of the output - side layers tend to be smaller , compared with multiplicative PyramidNets as illustrated in Figure [ reference ] . Previous works typically set multiplicative scaling of feature map dimension for downsampling modules , which is implemented to give a larger degree of freedom to the classification part by increasing the feature map dimension of the output - side layers . However , for our PyramidNet , the results in Figure [ reference ] implies that increasing the model capacity of the input - side layers would lead to a better performance improvement than using a conventional way of multiplicative scaling of feature map dimension . AUC : Area under ROC curve is a widely used metric in evaluating classification problems . Besides , some work validates AUC as a good measurement in CTR estimation [ reference ] . J Geog Syst 2002;4:85 - 97 ] . The modification takes the signal strength into account to reduce false positive detection in low grip force situations . The grip force measuring system and the force-change-detection algorithm allow measurement of the forces exerted by any number of fingers simultaneously without any constraints on finger position and are suitable for basic and clinical research in human and animal physiology as well as for psychophysics studies . In order for a driverless car to drive safe , it is important to accurately estimate information about any obstacles near the car and especially the existence and position of pedestrians . A histogram of oriented gradient ( HOG ) support vector machine ( SVM ) is widely used in pedestrian detection algorithms but can cause a false positive problem . This paper will suggest an improved pedestrian detecting means that is using Euclidean distance of coordinate values of the center in extracted foreground objectives after removing ground and background and in the extracted HOG based SVM from an input sequence through V-disparity map to achieve safe pedestrian detecting in dynamic conditions . The approach has been tested with real users ' inputs , noisy sensors and demanding activity sequences . Initial results have shown that complete and specialized activity models are properly learned with success rates of 100 % at the expense of learning some false positive models . Therefore , we have to ensure that the result is within the error tolerance range . In this paper , we suggest a method for a false-negative approach based on the Chernoff bound for efficient analysis of the data stream . Hence , we consider the problem of approximating frequency counts for space-efficient computation over data stream sliding windows . In this work , we used clinical dynamic WB 18F-FDG PET/CT studies to compare lesion detectability between Patlak graphical analysis and full compartmental modeling derived using a previously proposed methodology for hybrid macro- and micro-parametric imaging . The performance of these approaches in the context of lesion detection and false positive identification was compared to conventional static standardized uptake value ( SUV ) imaging . This study was conducted on 8 clinical whole-body 18F-FDG PET/CT studies injected with 3.5 MBq/kg of 18F-FDG . From the dispersion analysis , we found that variability is reduced more effectively by LWPM and RMA methods . From the false positive analysis , and for both parametric and nonparametric approaches , LWMM is found to perform best . Based on a complementary q-value analysis , LWMM approach again is the strongest candidate . The ASM provides a possibility for the selection of the predictor that probably has the best overall performance . The method presented in the paper applies to the ranking problem of any prediction software whose results can be properly represented in a true positive-false positive framework , thus providing a natural set-up for linear biological sequence analysis . MNC predicts three people where there are only two , and our method can only predict one instance due to a missing detection . MNC is unable to recover from a false positive detection and predicts two people . Second row : while both MNC and our method start off with poor bounding box localisation that does not cover the whole instance , we are able to segment the entire person , whereas MNC is bounded by its flawed region proposal . More importantly , occlusion caused by mask , sunglasses or other faces widely exists in the real - life applications . The difficulty to address the occlusion issue lies at the risk of potential false positive problem . Considering the case of detecting a face occluded by a sunglasses , only the lower part of face is available . The unobserved potential outcomes are treated as missing data and are imputed using empirical averages . Focusing on the binomial model , we demonstrate that the proposed imputed Girshick test achieves Type-I error and power control with both a fixed allocation ratio and an adaptive allocation such as Thompson Sampling through extensive experiments . In addition , we also run experiments on historical Etsy.com A/B tests to show the reduction in opportunity cost when using the proposed method . We propose a method for comparing the error rates of the base model with that of nearby models , and a p-value for testing whether a predictor is dispensable . We also propose a new quantity called model score which works similarly as the p-value for the control of type I error . Our proposal is closely related to the LOCO ( leave-one-covarate-out ) methods of ( [ Rinaldo 2016 Bootstrapping ] ) and less so , to Stability Selection ( [ Meinshausen 2010 stability ] ) . For rare variants , RarePedSim is the only program that can efficiently generate both genotypes and phenotypes , regardless of pedigree structure . Data generated by RarePedSim are in standard Linkage file ( .ped ) and Variant Call ( .vcf ) formats , ready to be used for a variety of purposes , including evaluation of type I error and power , for association methods including mixed models and linkage analysis methods . AVAILABILITY AND IMPLEMENTATION bioinformatics.org/simped/rare CONTACT sleal@bcm.edu . With an estimation procedure developed using numerical techniques in a companion paper , we study statistical operating characteristics of these extended models , especially those with non-linear effects . Type I error analysis shows the likelihood ratio test for GxM to be conservative in testing models extended from the bivariate Cholesky model , and to be liberal for models extended from the bivariate correlated factors model . Parameter estimation for all models is very good , with little bias exhibited for most models and parameters . Abstract Applied researchers often find themselves making statistical inferences in settings that would seem to require multiple comparisons adjustments . We challenge the Type I error paradigm that underlies these corrections . Moreover we posit that the problem of multiple comparisons can disappear entirely when viewed from a hierarchical Bayesian perspective . The problem is particularly serious in the US Food and Drug Administration regulation , but it is also appreciable in the European Medicines Agency one . For the partially replicated TRR/RTR/RRT and the replicated TRTR/RTRT crossover designs , we quantify these type I error problems by means of a simulation study , discuss their possible causes and propose straightforward improvements on both regulatory procedures that improve their type I error control while maintaining an adequate power . Copyright © 2015 John Wiley & Sons , Ltd. Applying complete enumeration which does not have any error , we confirm again the results by computing the sizes precisely on computer in moderate sample sizes . When the sample sizes are unbalanced , the peaks of the type I error rates occur at the extremes of the nuisance parameter . But , the type I error rates of the three tests are close to the nominal level in most values of the nuisance parameter except the extremes . This work studies error exponent limits in hypothesis testing ( HT ) in a distributed scenario with partial communication constraints . We derive general conditions on the Type I error restriction under which the error exponent of the optimal Type II error has a closed-form characterization for the task of testing against independence . We show that the error exponent is preserved for a family of decreasing Type I error restrictions . As the inspection process is itself imperfect , some perfect items can be classified as defective , known as type I inspection error , with known probability distribution and defective items can be classified as perfect , known as type II inspection error , with known probability distribution , during the inspection process . Because of type I inspection error , some perfect items can be dispose at discounted rate as defective items , leading to loss of revenue . Similarly , due to type II inspection error , some defective items can be supply to the buyer and sale in market as perfect items . ➁ Sensitivity to global amplitude differences : Signal ‘ L ’ is twice as strong in data set B compared to data set A. Back-projected spatial maps for ‘ L ’ are identical ( i.e. no detection of a significant difference between data sets A and B ; false negative detection ) . By comparison , the dual regression results ( with time series normalisation ) reflect differences in the global amplitude between the data sets ( true positive detection ) . An updating process of the background model is utilized to improve the robustness against illumination changes . However , the process sometimes causes a false-negative problem when a moving object stops in an observed scene . A paused object will be gradually trained as the background since the observed pixel value is directly used for the model update . False Positives ( FPs ) and False Negatives ( FNs ) happen to every Intrusion Detection/Prevention System ( IDS/IPS ) . This work proposes a mechanism of False Positive/Negative Assessment ( FPNA ) with multiple IDSs/IPSs to collect FP and FN cases from real-world traffic . Over a period of sixteen months , more than two thousand FPs and FNs have been collected and analyzed . Lossy encoding is used to further compress the data structure . While these approximation methods introduce additional errors to a strict Bayesian approach , we show how the errors can be both minimized and biased toward a false negative classification . We demonstrate a 6 × speedup over two well-known spam filters ( bogofilter and qsf ) while achieving an identical false positive rate and similar false negative rate to the original filters . In recent years , Deep Convolutional Neural Networks ( DCNN ) segmentation methods have demonstrated advantages for abdominal organ segmentation . However , variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods . In this paper , we propose the Splenomegaly Segmentation Network ( SSNet ) to address spatial variations when segmenting extraordinarily large spleens . This work had as main objective , the implementation and application of E-MODEL according to ITU-T G.107 , proposing a model that guarantees a good correlation between their estimates of quality assessment and estimates of quality assessment of PESQ . The performance evaluation of the proposed model has been implemented through the methodology described in ITU-T recommendation P.564 aimed at correlation analysis , error analysis , and analysis of false positive / negative , among the estimates of quality assessment of E -MODEL and PESQ . The model showed encouraging results with regard to performance analysis , especially in error analysis . Aiming at this problem , a hierarchical remote data possession checking ( hierarchical-remote data possession checking ( H-RDPC ) ) method is proposed , which can provide efficient and secure remote data integrity protection and can support dynamic data operations . This paper gives the algorithm descriptions , security , and false negative rate analysis of H-RDPC . The security analysis and experimental performance evaluation results show that the proposed H-RDPC is efficient and reliable in verifying massive cloud files , and it has 32–81 % improvement in performance compared with RDPC . Aiming at the LRU ( Least Recently Used ) algorithm 's problem of high false negative probability , a two-stage structure algorithm based on CBF ( Counting Bloom Filter ) and LRU is presented . According to the characteristics of the large bandwidth and long duration , the algorithm introduces time window mechanism to filter the probable large flow and saves the flow message in LRU for further screening . The algorithm is developed based on the wavelet transform , the dominant local orientation ( DLO ) and coherence that derived from the gradient of Gaussian . Computer simulation results show that while the rate of Type II error Incorrect recognition of two different fingerprints as identical fingerprints-is held at 0.0 % , the rate of Type I error-Incorrect recognition of two identical fingerprints as different ones-is 2.5 % . The first- and second-order optimum achievable exponents in the simple hypothesis testing problem are investigated . The optimum achievable exponent for type II error probability , under the constraint that the type I error probability is allowed asymptotically up to ε , is called the ε-optimum exponent . In this paper , we first give the second-order ε-exponent in the case where the null hypothesis and the alternative hypothesis are a mixed memoryless source and a stationary memoryless source , respectively . The precautionary principle . however , dictates that Type II errors are a serious problem for environmental management-and much more so than Type I errors . Thus , not detecting impacts ( Type II ) is not precautionary . A new output caps current estimator circuit is presented that accurately models a complex output filter network , including effective series inductance . System frequency response with combined output cap current estimator circuit and a type-II error amplifier shows a voltage mode type-III compensator . New compensator enables higher BW with sufficient phase margin limits . A reference effect is chosen based on its neurophysiological significance to the researcher . This method provides a means to quantitatively express alternative hypotheses for fMRI , thus allowing type II error assessment prior to the collection of fMRI data . The simultaneous control of both type I and type II error should make meaningful interpretations possible from both positive and negative fMRI results . Multiple hypothesis testing is a central topic in statistics , but despite abundant work on the false discovery rate ( FDR ) and the corresponding Type-II error concept known as the false non-discovery rate ( FNR ) , a fine-grained understanding of the fundamental limits of multiple testing has not been developed . Our main contribution is to derive a precise non-asymptotic tradeoff between FNR and FDR for a variant of the generalized Gaussian sequence model . The sensitivity analysis results indicate the model is very sensitive to shortage cost per unit shortage and type-I error of inspection . While findings of this study show that the model is very sensitive to parameters type-I error of inspection , proportion of defectives in production process and shortage cost per unit short , it is much less sensitive to parameters like proportion of scraps in rework process and Type-II error of inspection . If the existence of such error and shortages are ignored , then the obtained results may differ considerably from the optimal outcome . The size M of the message alphabet increases at most subexponentially with the number of observations . The asymptotic behavior of the type II error rate is investigated as the number of observations increases to infinity , and the associated error exponent is obtained under mild assumptions on the source distributions . This exponent is independent of the test level /spl epsiv/ and the actual codebook sizes M , is achieved by a universally optimal sequence of acceptance regions , and is characterized by an infimum of informational divergence rate over a class of infinite-dimensional distributions . The Receiver Operating Characteristic ( ROC ) surface is a generalization of the ROC curve and is widely used for assessment of the accuracy of diagnostic tests on three categories . Verification bias occurs when not all subjects have their labels observed . In particular , we develop a method to construct a scored ROC curve , demonstrate how to reduce it to a standard ROC curve , and illustrate how it can be used to compare learning models . Our experiments demonstrate that the scored ROC curve is capable of measuring similarities as well as differences in the performance of different learning models , and is more sensitive to them than the standard ROC curve . In addition , we illustrate our method 's ability to detect changes in data distribution between training and testing . Performance analysis of the classifiers has been carried out using independent validation method and the Receiver Operating Characteristic ( ROC ) method . Performance indicators used were Percentage Classification Accuracy ( PCLA ) , Mean Square Error ( MSE ) , Normalized MSE ( NMSE ) , Area under ROC curve ( AROC ) , Area under Convex Hull of ROC Curve ( AHROC ) , Sensitivity ( S ) , Specificity ( R ) , Predictive Pre and Predictive Post . From the comparative analysis of the classifiers , it is evident that the two hidden layer MLP NN gives the best classification accuracy at hidden layer comprising of 65 and 80 Processing Elements ( PE ) respectively when the combined feature matrix of SC and OP was used as input to the network . The Receiver Operating Characteristic ( ROC ) surface is a generalization of ROC curve and is widely used for assessment of the accuracy of diagnostic tests on three categories . A complication called the verification bias , meaning that not all subjects have their true disease status verified often occur in real application of ROC analysis . The estimated vulnerability index values of the CDI model were processed in a geographic information system ( GIS ) environment to produce a catastrophe theory – DRASTIC groundwater vulnerability to pollution index ( CDGVPI ) map , which demarcated the area into five vulnerability zones . The produced CDGVPI map was validated by applying the water quality status – vulnerability zone relationship ( WVR ) approach and the relative operating characteristic ( ROC ) curve method . The performance of the developed CDI model was compared with that of the standard DI model .
In this paper , it is also shown that the roughness in contours is a suitable characteristic feature for diagnosis of ill-defined malignant tumors with AUC equal to 0.94 in their classification . The extracted information was also found to be useful in the classification of early malignancies whereas in the classification of spiculated and ill-defined malignant masses in their early stage from those of benign tumors , we achieved high accuracy of 0.99 and 0.90 for AUC , respectively .
For all but the most problematic datasets , the algorithm also produced very good estimates of AUC standard error . The AUC estimates compared well with Wilcoxon estimates for continuously distributed data and are expected to be superior for categorical data . CONCLUSION This implementation of the PBM is reliable in a wide variety of ROC curve-fitting tasks .
TAN structures . Resulting models validity was assessed with in-sample AUC and stratified cross-validation , also comparing with previously published model . Overall , models achieved good discriminative power ( AUC>70 % ) and validity ( measures consistently above 70 % ) .
Sometime , the statistician or pharmacokineticist has to choose one particular method to calculate AUC depending on the actual concentration data . Among many pharmacokinetic approaches and modeling analyses , traditional trapezoidal , log-linear trapezoidal , Lagrange polynomial integration , and Purves method are commonly used in calculation of AUC . This paper will discuss the four methods of AUC calculation in pharmacokinetic studies of drug .
ABSTRACT The answer-until-correct ( AUC ) method of multiple-choice ( MC ) testing involves test respondents making selections until the keyed answer is identified . Despite attendant benefits that include improved learning , broad student adoption , and facile administration of partial credit , the use of AUC methods for classroom testing has been extremely limited . This study presents scoring properties and item analysis for 26 AUC university course examinations , administered using a commercial scratch-card response system .
Both classical logistics regression and AUC optimized method perform well in improving the model , but logistic regression still better in some aspects . AUC Optimized model has higher AUC than logistic regression model but has lower Kolmogorov-Smirnov Score .
Theorizing about treatment response was then done by evaluating the tree performance . Results The classification tree model has an area under the curve ( AUC ) classification performance of 67 % corresponding to a sensitivity of 0.69 and a specificity of 0.51 . This result in predicting patient-level response was slightly better than the logistic regression model featuring an AUC of 64 % ( 0.63 sensitivity and 0.54 specificity ) .
To this end , we introduce the new concepts of strict ranking representability , a restrictive condition that can be verified easily , and AUC ranking representability , a practically more useful condition that is more difficult to verify . However , the link between pairwise bipartite rankings and dice games allows us to formulate necessary transitivity conditions for AUC ranking representability . A sufficient condition on the other hand is obtained by introducing a new type of transitivity that can be verified by solving an integer quadratic program .
The features selected by FOS were used in a FOS predictor and nearest-neighbour predictor and had an area under the receiver operating curve ( AUC ) of 0.889 and 0.791 respectively . The FOS predictor AUC is significantly improved over the signal enhancement ratio predictor with an AUC of 0.706 ( p = 0.0035 for the difference in the AUCs ) . Moreover , using FOS-selected features in a support vector machine increased the AUC over that resulting when the features were manually selected .
We model the anomaly score function by a neural network-based unsupervised anomaly detection method , e.g. , autoencoders . The proposed method performs well even when only a small number of inexact labels are available by incorporating an unsupervised anomaly detection mechanism with inexact AUC maximization . Using various datasets , we experimentally demonstrate that our proposed method improves the anomaly detection performance with inexact anomaly labels , and outperforms existing unsupervised and supervised anomaly detection and multiple instance learning methods .
We now compare our method against the results in . In this paper they introduce a new measure of performance called AUC - 100 , which is something similar to normalized cumulative rewards up to 20 million frames . Table [ reference ] displays the results for our reference DQN and bootstrapped DQN as Boot - DQN .
We then describe one of the most powerful and widely used classifiers for high dimensional feature spaces ; the support vector machines ( SVM ) . We cover the area of classifier evaluation and comparison to provide practitioners with essential understanding of how to test , validate and select the appropriate models for their applications . Finally , we summarize the main advances in the field of ensemble learning , feature subset selection and feature subset ensembles .
In fact , even when Overall Accuracy is the classification metric , it is not the best metric to use within the wrapper . In addition , we find that there is no direct connection between the metric inside the wrapper and used for classification evaluation : the metrics show similar patterns across all four balance-aware metrics ( e.g. , all but Overall Accuracy ) .
We examine several variations to a TF*IDF-based approach for this task , empirically analyze their effects , and evaluate our system on a large collection of images from current news newsgroups . In addition , we investigate alternative classification and evaluation methods , and the effects that secondary features have on indoor/outdoor classification . Using density estimation over the raw TF*IDF values , we obtain a classification accuracy of 82 % , a number that outperforms baseline estimates and earlier , image-based approaches , at least in the domain of news articles , and that nears the accuracy of humans who perform the same task with access to comparable information .
To further advance the automatic process of land use/cover ( LULC ) classification extraction through remote sensing ( RS ) images , by reading many literatures , we summarized the problems , research difficulties and development trends in the process of information extraction and classification of land use . Overall , LULC Classification and extraction based on RS images include 3 tasks : data source selection , sampling design , classification method selection and classifier performance evaluation . These tasks are all important , that is , interdependence and mutual influence .
7.1 Introduction . 7.2 Classifying evaluation . 7.3 ' Quick And Dirty ' .
Moreover , the implication of evaluation depends on such criteria as evaluation needs , evaluation purpose , typically applied methods , the role of evaluator and others . The paper also provides a discussion on the evaluation in historical perspective and the classification of evaluation models . The article identifies and describes the main evaluation models such as politically controlled studies , objectives orientated evaluations , management information systems , decision orientated evaluations , etc .
Though good results have been achieved in the first round of undergraduate teaching evaluation , there are still a few problems with the assessment . A new round of evaluation should be tailored to the current situation and emphasis should be laid in the process on legalization of higher education evaluation , diversification of evaluation subjects and indexes , classification of the evaluation , standardization of the organization , regularization of the specialized evaluation , improvement of the accuracy of targeting in the evaluation , rectification of the thoughts and ideas of education , revision and adjustment of the evaluation criterion system so as to formulate a long-term higher education quality guaranteeing system .
Specifically , we conduct experiments on the widely - used fine - grained benchmark Caltech - UCSD birds dataset ( CUB200 - 2011 ) . The classification task is to discriminate among 200 species of birds , and is challenging for computer vision systems due to the high degree of similarity between categories . It contains 11 , 788 images of 200 bird species .
Doing this allows the network to retain spatial information and learn the parameters of these layers as in other convolutional layers . Lastly , neurons in the last layer ( i.e. , the classification layer ) are grouped into feature maps , where denotes the number of classes . The output of the classification layer is then converted into normalized probability values via a softmax function .
section : Relational Learning Relational classification ( or collective classification ) methods [ reference ][ reference ][ reference ][ reference ] use links between data items as part of the classification process . Exact inference in the collective classification problem is NP - hard , and solutions have focused on the use of approximate inference algorithm which may not be guaranteed to converge [ reference ] .
x0jj2Þ ð7Þ When classifying , Support Vector Machines separate the different classes of data by a hyper - plane contained by the decision function in Eq . ( 8 ) .
If not specified , all models are trained with data augmentation : randomly performs horizontal flips , and takes a random crop with 32 32 for CIFAR - 10 and CIFAR - 100 ( 28 28 for Fashion - MNIST ) from images padded by 4 pixels on each side . subsubsection : Classification Evaluation Classification accuracy on different datasets .
subsection : Evaluating DCGANs capability to capture data distributions We propose to apply standard classification metrics to a conditional version of our model , evaluating the conditional distributions learned . We trained a DCGAN on MNIST ( splitting off a 10 K validation set ) as well as a permutation invariant GAN baseline and evaluated the models using a nearest neighbor classifier comparing real data to a set of generated conditional samples .
Note that we only need to learn the parameters in the localisation network . One of the key contribution of STN is making the whole process differentiable , so that the localisation network can be directly optimized for the classification objective via back propagation . Please refer to for more technical details .
use boundary detection and hierarchical grouping before performing category segmentation . The common attribute in all these approaches is the use of hand engineered features for classification of either RGB or RGBD images . The success of deep convolutional neural networks for object classification has more recently led researchers to exploit their feature learning capabilities for structured prediction problems such as segmentation .
Because the feature map dimension increases linearly in the case of additive PyramidNets , the feature map dimensions of the input - side layers tend to be larger , and those of the output - side layers tend to be smaller , compared with multiplicative PyramidNets as illustrated in Figure [ reference ] . Previous works typically set multiplicative scaling of feature map dimension for downsampling modules , which is implemented to give a larger degree of freedom to the classification part by increasing the feature map dimension of the output - side layers . However , for our PyramidNet , the results in Figure [ reference ] implies that increasing the model capacity of the input - side layers would lead to a better performance improvement than using a conventional way of multiplicative scaling of feature map dimension .
AUC : Area under ROC curve is a widely used metric in evaluating classification problems . Besides , some work validates AUC as a good measurement in CTR estimation [ reference ] .
J Geog Syst 2002;4:85 - 97 ] . The modification takes the signal strength into account to reduce false positive detection in low grip force situations . The grip force measuring system and the force-change-detection algorithm allow measurement of the forces exerted by any number of fingers simultaneously without any constraints on finger position and are suitable for basic and clinical research in human and animal physiology as well as for psychophysics studies .
In order for a driverless car to drive safe , it is important to accurately estimate information about any obstacles near the car and especially the existence and position of pedestrians . A histogram of oriented gradient ( HOG ) support vector machine ( SVM ) is widely used in pedestrian detection algorithms but can cause a false positive problem . This paper will suggest an improved pedestrian detecting means that is using Euclidean distance of coordinate values of the center in extracted foreground objectives after removing ground and background and in the extracted HOG based SVM from an input sequence through V-disparity map to achieve safe pedestrian detecting in dynamic conditions .
The approach has been tested with real users ' inputs , noisy sensors and demanding activity sequences . Initial results have shown that complete and specialized activity models are properly learned with success rates of 100 % at the expense of learning some false positive models .
Therefore , we have to ensure that the result is within the error tolerance range . In this paper , we suggest a method for a false-negative approach based on the Chernoff bound for efficient analysis of the data stream . Hence , we consider the problem of approximating frequency counts for space-efficient computation over data stream sliding windows .
In this work , we used clinical dynamic WB 18F-FDG PET/CT studies to compare lesion detectability between Patlak graphical analysis and full compartmental modeling derived using a previously proposed methodology for hybrid macro- and micro-parametric imaging . The performance of these approaches in the context of lesion detection and false positive identification was compared to conventional static standardized uptake value ( SUV ) imaging . This study was conducted on 8 clinical whole-body 18F-FDG PET/CT studies injected with 3.5 MBq/kg of 18F-FDG .
From the dispersion analysis , we found that variability is reduced more effectively by LWPM and RMA methods . From the false positive analysis , and for both parametric and nonparametric approaches , LWMM is found to perform best . Based on a complementary q-value analysis , LWMM approach again is the strongest candidate .
The ASM provides a possibility for the selection of the predictor that probably has the best overall performance . The method presented in the paper applies to the ranking problem of any prediction software whose results can be properly represented in a true positive-false positive framework , thus providing a natural set-up for linear biological sequence analysis .
MNC predicts three people where there are only two , and our method can only predict one instance due to a missing detection . MNC is unable to recover from a false positive detection and predicts two people . Second row : while both MNC and our method start off with poor bounding box localisation that does not cover the whole instance , we are able to segment the entire person , whereas MNC is bounded by its flawed region proposal .
More importantly , occlusion caused by mask , sunglasses or other faces widely exists in the real - life applications . The difficulty to address the occlusion issue lies at the risk of potential false positive problem . Considering the case of detecting a face occluded by a sunglasses , only the lower part of face is available .
The unobserved potential outcomes are treated as missing data and are imputed using empirical averages . Focusing on the binomial model , we demonstrate that the proposed imputed Girshick test achieves Type-I error and power control with both a fixed allocation ratio and an adaptive allocation such as Thompson Sampling through extensive experiments . In addition , we also run experiments on historical Etsy.com A/B tests to show the reduction in opportunity cost when using the proposed method .
We propose a method for comparing the error rates of the base model with that of nearby models , and a p-value for testing whether a predictor is dispensable . We also propose a new quantity called model score which works similarly as the p-value for the control of type I error . Our proposal is closely related to the LOCO ( leave-one-covarate-out ) methods of ( [ Rinaldo 2016 Bootstrapping ] ) and less so , to Stability Selection ( [ Meinshausen 2010 stability ] ) .
For rare variants , RarePedSim is the only program that can efficiently generate both genotypes and phenotypes , regardless of pedigree structure . Data generated by RarePedSim are in standard Linkage file ( .ped ) and Variant Call ( .vcf ) formats , ready to be used for a variety of purposes , including evaluation of type I error and power , for association methods including mixed models and linkage analysis methods . AVAILABILITY AND IMPLEMENTATION bioinformatics.org/simped/rare CONTACT sleal@bcm.edu .
With an estimation procedure developed using numerical techniques in a companion paper , we study statistical operating characteristics of these extended models , especially those with non-linear effects . Type I error analysis shows the likelihood ratio test for GxM to be conservative in testing models extended from the bivariate Cholesky model , and to be liberal for models extended from the bivariate correlated factors model . Parameter estimation for all models is very good , with little bias exhibited for most models and parameters .
Abstract Applied researchers often find themselves making statistical inferences in settings that would seem to require multiple comparisons adjustments . We challenge the Type I error paradigm that underlies these corrections . Moreover we posit that the problem of multiple comparisons can disappear entirely when viewed from a hierarchical Bayesian perspective .
The problem is particularly serious in the US Food and Drug Administration regulation , but it is also appreciable in the European Medicines Agency one . For the partially replicated TRR/RTR/RRT and the replicated TRTR/RTRT crossover designs , we quantify these type I error problems by means of a simulation study , discuss their possible causes and propose straightforward improvements on both regulatory procedures that improve their type I error control while maintaining an adequate power . Copyright © 2015 John Wiley & Sons , Ltd.
Applying complete enumeration which does not have any error , we confirm again the results by computing the sizes precisely on computer in moderate sample sizes . When the sample sizes are unbalanced , the peaks of the type I error rates occur at the extremes of the nuisance parameter . But , the type I error rates of the three tests are close to the nominal level in most values of the nuisance parameter except the extremes .
This work studies error exponent limits in hypothesis testing ( HT ) in a distributed scenario with partial communication constraints . We derive general conditions on the Type I error restriction under which the error exponent of the optimal Type II error has a closed-form characterization for the task of testing against independence . We show that the error exponent is preserved for a family of decreasing Type I error restrictions .
As the inspection process is itself imperfect , some perfect items can be classified as defective , known as type I inspection error , with known probability distribution and defective items can be classified as perfect , known as type II inspection error , with known probability distribution , during the inspection process . Because of type I inspection error , some perfect items can be dispose at discounted rate as defective items , leading to loss of revenue . Similarly , due to type II inspection error , some defective items can be supply to the buyer and sale in market as perfect items .
➁ Sensitivity to global amplitude differences : Signal ‘ L ’ is twice as strong in data set B compared to data set A. Back-projected spatial maps for ‘ L ’ are identical ( i.e. no detection of a significant difference between data sets A and B ; false negative detection ) . By comparison , the dual regression results ( with time series normalisation ) reflect differences in the global amplitude between the data sets ( true positive detection ) .
An updating process of the background model is utilized to improve the robustness against illumination changes . However , the process sometimes causes a false-negative problem when a moving object stops in an observed scene . A paused object will be gradually trained as the background since the observed pixel value is directly used for the model update .
False Positives ( FPs ) and False Negatives ( FNs ) happen to every Intrusion Detection/Prevention System ( IDS/IPS ) . This work proposes a mechanism of False Positive/Negative Assessment ( FPNA ) with multiple IDSs/IPSs to collect FP and FN cases from real-world traffic . Over a period of sixteen months , more than two thousand FPs and FNs have been collected and analyzed .
Lossy encoding is used to further compress the data structure . While these approximation methods introduce additional errors to a strict Bayesian approach , we show how the errors can be both minimized and biased toward a false negative classification . We demonstrate a 6 × speedup over two well-known spam filters ( bogofilter and qsf ) while achieving an identical false positive rate and similar false negative rate to the original filters .
In recent years , Deep Convolutional Neural Networks ( DCNN ) segmentation methods have demonstrated advantages for abdominal organ segmentation . However , variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods . In this paper , we propose the Splenomegaly Segmentation Network ( SSNet ) to address spatial variations when segmenting extraordinarily large spleens .
This work had as main objective , the implementation and application of E-MODEL according to ITU-T G.107 , proposing a model that guarantees a good correlation between their estimates of quality assessment and estimates of quality assessment of PESQ . The performance evaluation of the proposed model has been implemented through the methodology described in ITU-T recommendation P.564 aimed at correlation analysis , error analysis , and analysis of false positive / negative , among the estimates of quality assessment of E -MODEL and PESQ . The model showed encouraging results with regard to performance analysis , especially in error analysis .
Aiming at this problem , a hierarchical remote data possession checking ( hierarchical-remote data possession checking ( H-RDPC ) ) method is proposed , which can provide efficient and secure remote data integrity protection and can support dynamic data operations . This paper gives the algorithm descriptions , security , and false negative rate analysis of H-RDPC . The security analysis and experimental performance evaluation results show that the proposed H-RDPC is efficient and reliable in verifying massive cloud files , and it has 32–81 % improvement in performance compared with RDPC .
Aiming at the LRU ( Least Recently Used ) algorithm 's problem of high false negative probability , a two-stage structure algorithm based on CBF ( Counting Bloom Filter ) and LRU is presented . According to the characteristics of the large bandwidth and long duration , the algorithm introduces time window mechanism to filter the probable large flow and saves the flow message in LRU for further screening .
The algorithm is developed based on the wavelet transform , the dominant local orientation ( DLO ) and coherence that derived from the gradient of Gaussian . Computer simulation results show that while the rate of Type II error Incorrect recognition of two different fingerprints as identical fingerprints-is held at 0.0 % , the rate of Type I error-Incorrect recognition of two identical fingerprints as different ones-is 2.5 % .
The first- and second-order optimum achievable exponents in the simple hypothesis testing problem are investigated . The optimum achievable exponent for type II error probability , under the constraint that the type I error probability is allowed asymptotically up to ε , is called the ε-optimum exponent . In this paper , we first give the second-order ε-exponent in the case where the null hypothesis and the alternative hypothesis are a mixed memoryless source and a stationary memoryless source , respectively .
The precautionary principle . however , dictates that Type II errors are a serious problem for environmental management-and much more so than Type I errors . Thus , not detecting impacts ( Type II ) is not precautionary .
A new output caps current estimator circuit is presented that accurately models a complex output filter network , including effective series inductance . System frequency response with combined output cap current estimator circuit and a type-II error amplifier shows a voltage mode type-III compensator . New compensator enables higher BW with sufficient phase margin limits .
A reference effect is chosen based on its neurophysiological significance to the researcher . This method provides a means to quantitatively express alternative hypotheses for fMRI , thus allowing type II error assessment prior to the collection of fMRI data . The simultaneous control of both type I and type II error should make meaningful interpretations possible from both positive and negative fMRI results .
Multiple hypothesis testing is a central topic in statistics , but despite abundant work on the false discovery rate ( FDR ) and the corresponding Type-II error concept known as the false non-discovery rate ( FNR ) , a fine-grained understanding of the fundamental limits of multiple testing has not been developed . Our main contribution is to derive a precise non-asymptotic tradeoff between FNR and FDR for a variant of the generalized Gaussian sequence model .
The sensitivity analysis results indicate the model is very sensitive to shortage cost per unit shortage and type-I error of inspection . While findings of this study show that the model is very sensitive to parameters type-I error of inspection , proportion of defectives in production process and shortage cost per unit short , it is much less sensitive to parameters like proportion of scraps in rework process and Type-II error of inspection . If the existence of such error and shortages are ignored , then the obtained results may differ considerably from the optimal outcome .
The size M of the message alphabet increases at most subexponentially with the number of observations . The asymptotic behavior of the type II error rate is investigated as the number of observations increases to infinity , and the associated error exponent is obtained under mild assumptions on the source distributions . This exponent is independent of the test level /spl epsiv/ and the actual codebook sizes M , is achieved by a universally optimal sequence of acceptance regions , and is characterized by an infimum of informational divergence rate over a class of infinite-dimensional distributions .
The Receiver Operating Characteristic ( ROC ) surface is a generalization of the ROC curve and is widely used for assessment of the accuracy of diagnostic tests on three categories . Verification bias occurs when not all subjects have their labels observed .
In particular , we develop a method to construct a scored ROC curve , demonstrate how to reduce it to a standard ROC curve , and illustrate how it can be used to compare learning models . Our experiments demonstrate that the scored ROC curve is capable of measuring similarities as well as differences in the performance of different learning models , and is more sensitive to them than the standard ROC curve . In addition , we illustrate our method 's ability to detect changes in data distribution between training and testing .
Performance analysis of the classifiers has been carried out using independent validation method and the Receiver Operating Characteristic ( ROC ) method . Performance indicators used were Percentage Classification Accuracy ( PCLA ) , Mean Square Error ( MSE ) , Normalized MSE ( NMSE ) , Area under ROC curve ( AROC ) , Area under Convex Hull of ROC Curve ( AHROC ) , Sensitivity ( S ) , Specificity ( R ) , Predictive Pre and Predictive Post . From the comparative analysis of the classifiers , it is evident that the two hidden layer MLP NN gives the best classification accuracy at hidden layer comprising of 65 and 80 Processing Elements ( PE ) respectively when the combined feature matrix of SC and OP was used as input to the network .
The Receiver Operating Characteristic ( ROC ) surface is a generalization of ROC curve and is widely used for assessment of the accuracy of diagnostic tests on three categories . A complication called the verification bias , meaning that not all subjects have their true disease status verified often occur in real application of ROC analysis .
The estimated vulnerability index values of the CDI model were processed in a geographic information system ( GIS ) environment to produce a catastrophe theory – DRASTIC groundwater vulnerability to pollution index ( CDGVPI ) map , which demarcated the area into five vulnerability zones . The produced CDGVPI map was validated by applying the water quality status – vulnerability zone relationship ( WVR ) approach and the relative operating characteristic ( ROC ) curve method . The performance of the developed CDI model was compared with that of the standard DI model .
An Efficient Fractal Method for Detection and Diagnosis of Breast Masses in Mammograms Reliable and computationally efficient maximum-likelihood estimation of "proper" binormal ROC curves. Obstructive Sleep Apnea Diagnosis: The Bayesian Network Model Revisited SAS Programming to Calculate AUC in Pharmacokinetic Studies —Comparison of Four Methods in Concentration Data Partial Credit in Answer-Until-Correct Multiple-Choice Tests Deployed in a Classroom Setting Credit Scoring Refinement Using Optimized Logistic Regression Patient-Level Effectiveness Prediction Modeling for Glioblastoma Using Classification Trees A transitivity analysis of bipartite rankings in pairwise multi-class classification Feature Selection in Computer-Aided Breast Cancer Diagnosis via Dynamic Contrast-Enhanced Magnetic Resonance Images Anomaly detection with inexact labels Deep Exploration via Bootstrapped DQN Novel Machine Learning Techniques for Micro-Array Data Classification The importance of performance metrics within wrapper feature selection Text-based approaches for non-topical image categorization Present Situation and Trend of Remote Sensing Land Use/Cover Classification Extraction Mobile Interaction Design Evaluation in Public Management: the Conception and Models A Probe into the Undergraduate Teaching Evaluation System with Its Distinctive Chinese Characteristics Part-Based R-CNNs for Fine-Grained Category Detection Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation DeepWalk: online learning of social representations Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification Random Erasing Data Augmentation Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Deep Pyramidal Residual Networks Product-Based Neural Networks for User Response Prediction Detection of changes in grip forces on a sliding object Improved HOG Pedestrian Detection Means Using V-disparity Map Extending knowledge-driven activity models through data-driven learning techniques A sliding window-based false-negative approach for ubiquitous data stream analysis Clinical assessment of lesion detectability in dynamic whole-body PET imaging using compartmental and Patlak parametric mapping Comparison of microarray preprocessing methods. Comparing the Success of Different Prediction Software in Sequence Analysis: A Review Holistic, Instance-Level Human Parsing Face Attention Network: An Effective Face Detector for the Occluded Faces A Sequential Test for Selecting the Better Variant: Online A/B testing, Adaptive Allocation, and Continuous Monitoring Post model-fitting exploration via a "Next-Door" analysis Generation of sequence-based data for pedigree-segregating Mendelian or Complex traits Comparing Alternative Biometric Models with and without Gene-by-Measured Environment Interaction in Behavior Genetic Designs: Statistical Operating Characteristics Why We (Usually) Don't Have to Worry About Multiple Comparisons Consumer's risk in the EMA and FDA regulatory approaches for bioequivalence in highly variable drugs. The sizes of the three popular asymptotic tests for testing homogeneity of two binomial proportions New Results on Testing Against Independence with Rate-Limited Constraints Integrated single-vendor single-buyer inventory model with imperfect production and inspection by vendor Group comparison of resting-state FMRI data using multi-subject ICA and dual regression Maintenance of Blind Background Model for Robust Object Detection False Positives and Negatives from Real Traffic with Intrusion Detection/Prevention Systems Speed Up Statistical Spam Filter by Approximation Splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks Métodos de Avaliação de QoS em Serviços de Voz sobre Redes IP Hierarchical remote data possession checking method based on massive cloud files New Algorithm Based on LRU for Identify and Measuring Large Flows Automatic real-time identification of fingerprint images using wavelet transform and gradient of Gaussian First- and second-order hypothesis testing for mixed memoryless sources with general mixture Environmental decision-making and the precautionary principle: what does this principle mean in environmental sampling practice? The analysis of multi-phase current feedforward type-III constant on-time control with ultrafast load transient response for voltage regulator modules A Reference Effect Approach for Power Analysis in fMRI Optimal Rates and Tradeoffs in Multiple Testing ECONOMIC PRODUCTION QUANTITY IN REWORKABLE PRODUCTION SYSTEMS WITH INSPECTION ERRORS, SCRAPS AND BACKLOGGING Error exponents for distributed detection of Markov sources Bayesian Semiparametric ROC surface estimation under verification bias Evaluating machine learning methods: scored receiver operating characteristics (sroc) curves Design of Early Fatigue Detection Elements of a Wearable Computing System for the Prevention of Road Accidents Bayesian ROC surface estimation under verification bias Development of a GIS-based catastrophe theory model (modified DRASTIC model) for groundwater vulnerability assessment
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