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A Support Vector Machine-Based Technique for Instance Selection

The field of instance selection (IS) concerns the determination of an optimal subset of data that has two fundamental properties: (i) the new obtained set is smaller than the original data set; and (ii) it retains the essential set of instances needed to build an accurate classifier. This work proposes a new IS approach based on the support vector machine (SVM) clustering technique. Our new technique employs supervised clustering SVM to determine the support vectors of potential clusters present in the data. Once these elements are established, they are marked as the instances to be added to the new reduced training set to be informed to the classifiers. We want to answer research questions such as: (1) Does SVM provide a strong IS technique? (2) Do the results outperform well-established techniques? (3) Is SVM-IS robust when the data set size increases? To answer these questions, we apply the SVM-IS algorithm to several data sets varying in number of instances, attributes and classes. The proposed method is also compared with well-established IS algorithms. The experiments show that our mechanism does not effectively reduce the original data sets, as in overlapping classes SVM tends to produce a large number of support vectors. Further investigation regarding the reduction of the support vectors in the final classification is necessary.