Feature subset selection: A correlation-based SVM filter approach

Boyang Li, Qiangwei Wang, Jinglu Hu*


研究成果: Article査読

15 被引用数 (Scopus)


The central criterion of feature selection is that good feature sets contain features that are highly correlated with the output, yet uncorrelated with each other. Based on this criterion, we address the problem of feature selection through correlation-based feature clustering and support vector machine (SVM) based feature ranking. Correlation-based clustering is proposed to group features into some clusters based on the correlation between two features. As a result, a feature is highly correlated to any other feature in the same cluster but uncorrelated to the features in other clusters. From each cluster, we select a feature as the delegate based on its influence quantities on the output. The influence quantities are measured by the feature sensitivity in the SVM. The proposed approach can identify relevant features and eliminate redundancy among them effectively. The effectiveness of the proposed approach is demonstrated through comparisons with other methods using real-world data with different dimensions.

ジャーナルIEEJ Transactions on Electrical and Electronic Engineering
出版ステータスPublished - 2011 3月

ASJC Scopus subject areas

  • 電子工学および電気工学


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