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 whi...
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Series: | Journal of Applied Mathematics |
Online Access: | http://dx.doi.org/10.1155/2013/416320 |
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doaj-9dae70dcdf814b0aa99594087a1e1fda2020-11-24T22:34:24ZengHindawi LimitedJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/416320416320A Semisupervised Feature Selection with Support Vector MachineKun Dai0Hong-Yi Yu1Qing Li2National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450002, ChinaNational Digital Switching System Engineering and Technological Research Center, Zhengzhou 450002, ChinaNational Digital Switching System Engineering and Technological Research Center, Zhengzhou 450002, ChinaFeature 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.http://dx.doi.org/10.1155/2013/416320 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kun Dai Hong-Yi Yu Qing Li |
spellingShingle |
Kun Dai Hong-Yi Yu Qing Li A Semisupervised Feature Selection with Support Vector Machine Journal of Applied Mathematics |
author_facet |
Kun Dai Hong-Yi Yu Qing Li |
author_sort |
Kun Dai |
title |
A Semisupervised Feature Selection with Support Vector Machine |
title_short |
A Semisupervised Feature Selection with Support Vector Machine |
title_full |
A Semisupervised Feature Selection with Support Vector Machine |
title_fullStr |
A Semisupervised Feature Selection with Support Vector Machine |
title_full_unstemmed |
A Semisupervised Feature Selection with Support Vector Machine |
title_sort |
semisupervised feature selection with support vector machine |
publisher |
Hindawi Limited |
series |
Journal of Applied Mathematics |
issn |
1110-757X 1687-0042 |
publishDate |
2013-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2013/416320 |
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