FS_KPARD: An effective SVM feature selection method

This paper presents an effective feature selection method for support vector machine (SVM). Unlike the traditional combinatorial searching method, feature selection is translated into the model selection of SVM which has been well studied. In more detail, the basic idea of this method is to tune the parameters of the Gaussian ARD (Automatic Relevance Determination) kernel via optimization of kernel polarization, and then to rank all features in decreasing order of importance so that more relevant features can be identified. The proposed method is tested on two UCI data sets to demonstrate its effectiveness.