Improved feature selection algorithm based on SVM and correlation

Authors:
Zong-Xia Xie;Qing-Hua Hu;Da-Ren Yu
Affiliations:
Harbin Institute of Technology, Harbin, P.R. China;Harbin Institute of Technology, Harbin, P.R. China;Harbin Institute of Technology, Harbin, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

As a feature selection method, support vector machines-recursive feature elimination (SVM-RFE) can remove irrelevance features but don’t take redundant features into consideration. In this paper, it is shown why this method can’t remove redundant features and an improved technique is presented. Correlation coefficient is introduced to measure the redundancy in the selected subset with SVM-RFE. The features which have a great correlation coefficient with some important feature are removed. Experimental results show that there actually are several strongly redundant features in the selected subsets by SVM-RFE. The coefficients are high to 0.99. The proposed method can not only reduce the number of features, but also keep the classification accuracy.