IPCM separability ratio for supervised feature selection

Authors:
Wing W. Y. Ng;Jun Wang;Daniel S. Yeung
Affiliations:
School of Computer Science and Engineering, South China University of Technology, Guangzhou, China;Dept. of Comp. Sci. and Tech., Shenzhen Graduate School, Harbin Institute of Technology, China;School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

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Abstract

Collecting data is very easy now owing to fast computers and ease of Internet access. It raises the problem of the curse of dimensionality to supervised classification problems. In our previous work, an Intra-Prototype / Inter-Class Separability Ratio (IPICSR) model is proposed to select relevant features for semi-supervised classification problems. In this work, a new margin based feature selection model is proposed based on the IPICSR model for supervised classification problems. Owing to the nature of supervised classification problems, a more accurate class separating margin could be found by the classifier. We adopt this advantage in the new Intra-Prototype / Class Margin Separability Ratio (IPCMSR) model. Experimental results are promising when compared to several existing methods using 4 UCI datasets.