Feature selection using SVM probabilistic outputs

Authors:
Kai Quan Shen;Chong Jin Ong;Xiao Ping Li;Hui Zheng;Einar P. V. Wilder-Smith
Affiliations:
Department of Mechanical Engineering, National University of Singapore, EA, Singapore;Department of Mechanical Engineering, National University of Singapore, EA, Singapore;Department of Mechanical Engineering and Division of Bioengineering, National University of Singapore, EA, Singapore;Department of Mechanical Engineering, National University of Singapore, EA, Singapore;Department of Medicine, National University of Singapore, Singapore
Venue:
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Year:
2006

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Abstract

A ranking criterion based on the posterior probability is proposed for feature selection on support vector machines (SVM). This criterion has the advantage that it is directly related to the importance of the features. Four approximations are proposed for the evaluation of this criterion. The performances of these approximations, used in the recursive feature elimination (RFE) approach, are evaluated on various artificial and real-world problems. Three of the proposed approximations show good performances consistently, with one having a slight edge over the other two. Their performances compare favorably with feature selection methods in the literature.