Feature subset selection for support vector machines through sensitivity analysis
Defeng Wang, Patrick P. K. Chan, D.S. Yeung, Eric C.C. Tsang · 2005
In the context of support vector machines, feature selection is motivated mainly by the consideration of classification speed and generalization ability. Sensitivity analysis of MLP and RBF has already been successfully applied in feature subset selection. We present a novel feature selection method for support vector machines (SVMs) using the sensitivity analysis of SVMs, which is defined as the deviation of separation margin with respect to the perturbation of given feature. The method we proposed can directly be applied to multi-class SVMs. Our experiments validate that the proposed strategy produces satisfactory results both on artificial and real-world data.