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

Authors:
Tinghua Wang;Houkuan Huang;Shengfeng Tian;Jianfeng Xu
Affiliations:
School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, PR China and School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, PR Ch ...;School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, PR China;School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, PR China;School of Software, Nanchang University, Nanchang 330047, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

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.