Systems, methods, and computer-readable media for building ensemble members of a Support Vector Machine (SVM) ensemble in parallel and executing processing in parallel on data allocated to each ensemble member are disclosed. The parallel construction and processing of data of each ensemble member allows a single large SVM calculation to be replaced with many smaller SVM calculations performed in parallel, and thus, may reduce the computational resources required to classify datasets.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for constructing a Support Vector Machine (SVM) ensemble, the method comprising: determining a subset of data in a training dataset to allocate to an ensemble member of the SVM ensemble; and building the ensemble member, wherein building the ensemble member comprises: partitioning a first portion of the subset of data into a first classification and a second portion of the subset of data into a second classification; determining a first plurality of data clusters among the first portion of the subset of data partitioned into the first classification and a second plurality of data clusters among the second portion of the subset of data partitioned into the second classification; identifying a first cross-classification cluster matchup, wherein the first cross-classification cluster matchup comprises a first data cluster of the first plurality of data clusters and a second data cluster of the second plurality of data clusters; identifying a second cross-classification cluster matchup, wherein the second cross-classification cluster matchup comprises a third data cluster of the first plurality of data clusters and a fourth data cluster of the second plurality of data clusters; and determining a first SVM for the first cross-classification cluster matchup and a second SVM for the second cross-classification cluster matchup, wherein the first SVM and the second SVM are determined at least partially in parallel. 2. The method of claim 1, wherein the training dataset is a first dataset, and wherein building the ensemble member further comprises: constructing a directed acyclic graph (DAG) using the first SVM and the second SVM, the method further comprising: classifying, by the ensemble member, a row vector of a second dataset in one of the first classification or the second classification using the DAG. 3. The method of claim 1, wherein the ensemble member is a first ensemble member and the subset of data in the training dataset is a first subset of data, the method further comprising: determining a second subset of data in the training dataset to allocate to a second ensemble member of the SVM ensemble; and building the second ensemble member, wherein building the second ensemble member comprises: partitioning a first portion of the second subset of data into the first classification and a second portion of the second subset of data into the second classification; determining a third plurality of data clusters among the first portion of the second subset of data partitioned into the first classification and a fourth plurality of data clusters among the second portion of the second subset of data partitioned into the second classification; identifying a third cross-classification cluster matchup, wherein the third cross-classification cluster matchup comprises a first data cluster of the third plurality of data clusters and a second data cluster of the fourth plurality of data clusters; identifying a fourth cross-classification cluster matchup, wherein the fourth cross-classification cluster matchup comprises a third data cluster of the third plurality of data clusters and a fourth data cluster of the fourth plurality of data clusters; and determining a third SVM for the third cross-classification cluster matchup and a fourth SVM for the fourth cross-classification cluster matchup, wherein the third SVM and the fourth SVM are determined at least partially in parallel. 4. The method of claim 3, wherein the first SVM, the second SVM, the third SVM, and the fourth SVM are each determined at least partially in parallel. 5. The method of claim 3, wherein the SVM ensemble comprises a plurality of ensemble members including the first ensemble member and the second ensemble member, the method further comprising: determining a first class confusion matrix for the first ensemble member; determining a second class confusion matrix for the second ensemble member; and determining an overall class confusion matrix for the SVM ensemble based at least in part on a set of classification results determined by the SVM ensemble for the training dataset, wherein each classification result is a classification determined by a threshold number of the plurality of ensemble members for a respective observation in the training dataset. 6. The method of claim 5, further comprising: determining, based at least in part on the first class confusion matrix and the second class confusion matrix, that a first classification error associated with the first ensemble member is greater than a second classification error associated with the second ensemble member; assigning a first weight to the first ensemble member based at least in part on the first class confusion matrix; and assigning a second weight to the second ensemble member based at least in part on the second class confusion matrix, wherein the first weight is less than the second weight. 7. The method of claim 1, wherein building the ensemble member further comprises: filtering the first cross-classification cluster matchup to remove a first one or more row vectors that are not candidates for use as a support vector for the first SVM; and filtering the second cross-classification cluster matchup to remove a second one or more row vectors that are not candidates for use as a support vector for the second SVM, wherein the first cross-classification cluster matchup and the second cross-classification cluster matchup are filtered at least partially in parallel. 8. A system for constructing a Support Vector Machine (SVM) ensemble, the system comprising: at least one memory storing computer-executable instructions; and at least one processor configured to access the at least one memory and execute the computer-executable instructions to: determine a subset of data in a training dataset to allocate to an ensemble member of the SVM ensemble; and perform a build of the ensemble member by: partitioning a first portion of the subset of data into a first classification and a second portion of the subset of data into a second classification; determining a first plurality of data clusters among the first portion of the subset of data partitioned into the first classification and a second plurality of data clusters among the second portion of the subset of data partitioned into the second classification; identifying a first cross-classification cluster matchup, wherein the first cross-classification cluster matchup comprises a first data cluster of the first plurality of data clusters and a second data cluster of the second plurality of data clusters; identifying a second cross-classification cluster matchup, wherein the second cross-classification cluster matchup comprises a third data cluster of the first plurality of data clusters and a fourth data cluster of the second plurality of data clusters; and determining a first SVM for the first cross-classification cluster matchup and a second SVM for the second cross-classification cluster matchup, wherein the first SVM and the second SVM are determined at least partially in parallel. 9. The system of claim 8, wherein the training dataset is a first dataset, and wherein the at least one processor is further configured to perform the build of the ensemble member by executing the computer-executable instructions to: construct a directed acyclic graph (DAG) using the first SVM and the second SVM, and wherein the at least one processor is further configured to execute the computer-executable instructions to: classify a row vector of a second dataset in one of the first classification or the second classification using the DAG. 10. The system of claim 8, wherein the ensemble member is a first ensemble member and the subset of data in the training dataset is a first subset of data, and wherein the at least one processor is further configured to execute the computer-executable instructions to: determine a second subset of data in the training dataset to allocate to a second ensemble member of the SVM ensemble; and perform a build of the second ensemble member by: partitioning a first portion of the second subset of data into the first classification and a second portion of the second subset of data into the second classification; determining a third plurality of data clusters associated with the first classification and a fourth plurality of data clusters associated with the second classification; identifying a third cross-classification cluster matchup, wherein the third cross-classification cluster matchup comprises a first data cluster of the third plurality of data clusters and a second data cluster of the fourth plurality of data clusters; identifying a fourth cross-classification cluster matchup, wherein the fourth cross-classification cluster matchup comprises a third data cluster of the third plurality of data clusters and a fourth data cluster of the fourth plurality of data clusters; and determining a third SVM for the third cross-classification cluster matchup and a fourth SVM for the fourth cross-classification cluster matchup, wherein the third SVM and the fourth SVM are determined at least partially in parallel. 11. The system of claim 10, wherein the first SVM, the second SVM, the third SVM, and the fourth SVM are each determined at least partially in parallel. 12. The system of claim 10, wherein the SVM ensemble comprises a plurality of ensemble members including the first ensemble member and the second ensemble member, and wherein the at least one processor is further configured to execute the computer-executable instructions to: determine a first class confusion matrix for the first ensemble member; determine a second class confusion matrix for the second ensemble member; and determine an overall class confusion matrix for the SVM ensemble based at least in part on a set of classification results determined by the SVM ensemble for the training dataset, wherein each classification result is a classification determined by a threshold number of the plurality of ensemble members for a respective observation in the training dataset. 13. The system of claim 12, wherein the at least one processor is further configured to execute the computer-executable instructions to: determine, based at least in part on the first class confusion matrix and the second class confusion matrix, that a first classification error associated with the first ensemble member is greater than a second classification error associated with the second ensemble member; assign a first weight to the first ensemble member based at least in part on the first class confusion matrix; and assign a second weight to the second ensemble member based at least in part on the second class confusion matrix, wherein the first weight is less than the second weight. 14. The system of claim 8, wherein the at least one processor is further configured to perform the build of the ensemble member by executing the computer-executable instructions to: filter the first cross-classification cluster matchup to remove a first one or more row vectors that are not candidates for use as a support vector for the first SVM; and filter the second cross-classification cluster matchup to remove a second one or more row vectors that are not candidates for use as a support vector for the second SVM, wherein the first cross-classification cluster matchup and the second cross-classification cluster matchup are filtered at least partially in parallel. 15. A computer program product for constructing a Support Vector Machine (SVM) ensemble, the computer program product comprising a non-transitory storage medium readable by a processing circuit, the storage medium storing instructions executable by the processing circuit to cause a method to be performed, the method comprising: determining a subset of data in a training dataset to allocate to an ensemble member of the SVM ensemble; and building the ensemble member, wherein building the ensemble member comprises: partitioning a first portion of the subset of data into a first classification and a second portion of the subset of data into a second classification; determining a first plurality of data clusters among the first portion of the subset of data partitioned into the first classification and a second plurality of data clusters among the second portion of the subset of data partitioned into the second classification; identifying a first cross-classification cluster matchup, wherein the first cross-classification cluster matchup comprises a first data cluster of the first plurality of data clusters and a second data cluster of the second plurality of data clusters; identifying a second cross-classification cluster matchup, wherein the second cross-classification cluster matchup comprises a third data cluster of the first plurality of data clusters and a fourth data cluster of the second plurality of data clusters; and determining a first SVM for the first cross-classification cluster matchup and a second SVM for the second cross-classification cluster matchup, wherein the first SVM and the second SVM are determined at least partially in parallel. 16. The computer program product of claim 15, wherein the training dataset is a first dataset, and wherein building the ensemble member further comprises: constructing a directed acyclic graph (DAG) using the first SVM and the second SVM, the method further comprising: classifying, by the ensemble member, a row vector of a second dataset in one of the first classification or the second classification using the DAG. 17. The computer program product of claim 15, wherein the ensemble member is a first ensemble member and the subset of data in the training dataset is a first subset of data, the method further comprising: determining a second subset of data in the training dataset to allocate to a second ensemble member of the SVM ensemble; and building the second ensemble member, wherein building the second ensemble member comprises: partitioning a first portion of the second subset of data into the first classification and a second portion of the second subset of data into the second classification; determining a third plurality of data clusters among the first portion of the second subset of data partitioned into the first classification and a fourth plurality of data clusters among the second portion of the second subset of data partitioned into the second classification; identifying a third cross-classification cluster matchup, wherein the third cross-classification cluster matchup comprises a first data cluster of the third plurality of data clusters and a second data cluster of the fourth plurality of data clusters; identifying a fourth cross-classification cluster matchup, wherein the fourth cross-classification cluster matchup comprises a third data cluster of the third plurality of data clusters and a fourth data cluster of the fourth plurality of data clusters; and determining a third SVM for the third cross-classification cluster matchup and a fourth SVM for the fourth cross-classification cluster matchup, wherein the third SVM and the fourth SVM are determined at least partially in parallel. 18. The computer program product of claim 17, wherein the first SVM, the second SVM, the third SVM, and the fourth SVM are each determined at least partially in parallel. 19. The computer program product of claim 17, wherein the SVM ensemble comprises a plurality of ensemble members including the first ensemble member and the second ensemble member, the method further comprising: determining a first class confusion matrix for the first ensemble member; determining a second class confusion matrix for the second ensemble member; and determining an overall class confusion matrix for the SVM ensemble based at least in part on a set of classification results determined by the SVM ensemble for the training dataset, wherein each classification result is a classification determined by a threshold number of the plurality of ensemble members for a respective observation in the training dataset. 20. The computer program product of claim 19, further comprising: determining, based at least in part on the first class confusion matrix and the second class confusion matrix, that a first classification error associated with the first ensemble member is greater than a second classification error associated with the second ensemble member; assigning a first weight to the first ensemble member based at least in part on the first class confusion matrix; and assigning a second weight to the second ensemble member based at least in part on the second class confusion matrix, wherein the first weight is less than the second weight.