Feature selection for SVM via optimization of kernel polarization with Gaussian ARD kernels

Feature selection aims at determining a subset of available features which is most discriminative and informative for data analysis. 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 hyperparameters of the Gaussian Automatic Relevance Determination (ARD) kernels via optimization of kernel polarization, and then to rank all features in decreasing order of importance so that more relevant features can be identified. We test the proposed method with some UCI machine learning benchmark examples and show that it can dramatically reduce the number of features and outperforms SVM trained using the features selected according to correlation coefficient and using all features.