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Journal of Applied Mathematics
Volume 2013, Article ID 416320, 11 pages
Research Article

A Semisupervised Feature Selection with Support Vector Machine

National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450002, China

Received 15 May 2013; Accepted 4 October 2013

Academic Editor: Antonio J. M. Ferreira

Copyright © 2013 Kun Dai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Feature selection has proved to be a beneficial tool in learning problems with the main advantages of interpretation and generalization. Most existing feature selection methods do not achieve optimal classification performance, since they neglect the correlations among highly correlated features which all contribute to classification. In this paper, a novel semisupervised feature selection algorithm based on support vector machine (SVM) is proposed, termed SENFS. In order to solve SENFS, an efficient algorithm based on the alternating direction method of multipliers is then developed. One advantage of SENFS is that it encourages highly correlated features to be selected or removed together. Experimental results demonstrate the effectiveness of our feature selection method on simulation data and benchmark data sets.