Journal of Applied Mathematics
- J. Appl. Math.
- Volume 2013 (2013), Article ID 416320, 11 pages.
A Semisupervised Feature Selection with Support Vector Machine
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.
J. Appl. Math., Volume 2013 (2013), Article ID 416320, 11 pages.
First available in Project Euclid: 14 March 2014
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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