Feature subset selection for support vector machines using confident margin

The aim of this study is to develop a feature subset selection (FSS) method based on the margin of support vector machines (SVM). The problem of directly using the SVM margin is that it does not always provide clear relationship between its value and the performance of SVM, and the best obtained subset is not guaranteed to be the best possible one. In this paper, a new solution is describe by the introduction of the confident margin (CM) in the subset criterion, which permits to get near the best recognition rate by monitoring the peak of CM curve without directly calculating the recognition rate, in order to save computational time. The performance of the proposed method was evaluated in artificial and real-world data experiments.