Data selection for support vector machine classifiers
Glenn Fung, Olvi L. Mangasarian · 2000
The problem of extracting a minimal number of data points from a large dataset, in order to generate a support vector machine (SVM) classifier, is formulated as a concave min-imization problem and solved by a finite number of linear programs. This minimal set of data points, which is the smallest number of support vectors that completely char-acterize a separating plane classifier, is considerably smaller than that required by a standard 1-norm support vector ma-chine with or without feature selection. The proposed ap-proach also incorporates a feature selection procedure that results in a minimal number of input features used by the classifier. Tenfold cross validation gives as good or better test results using the proposed minimal support vector ma-chine (MSVM) classifier based on the smaller set of data points compared to a standard 1-norm support vector ma-chine classifier. The reduction in data points used by an MSVM classifier over those used by a 1-norm SVM classifier averaged 66 % on seven public datasets and was as high as 81%. This makes MSVM a useful incremental classification tool which maintains only a small fraction of a large dataset before merging and processing it with new incoming data. Keywords support vector machines, data classification, data selection, concave minimization, linear programming 1.