Journal of Applied Mathematics

A Semisupervised Feature Selection with Support Vector Machine

Kun Dai, Hong-Yi Yu, and Qing Li

Full-text: Open access

Abstract

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.

Article information

Source
J. Appl. Math., Volume 2013 (2013), Article ID 416320, 11 pages.

Dates
First available in Project Euclid: 14 March 2014

Permanent link to this document
https://projecteuclid.org/euclid.jam/1394808269

Digital Object Identifier
doi:10.1155/2013/416320

Mathematical Reviews number (MathSciNet)
MR3138962

Zentralblatt MATH identifier
06950661

Citation

Dai, Kun; Yu, Hong-Yi; Li, Qing. A Semisupervised Feature Selection with Support Vector Machine. J. Appl. Math. 2013 (2013), Article ID 416320, 11 pages. doi:10.1155/2013/416320. https://projecteuclid.org/euclid.jam/1394808269


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