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Computational Intelligence and Neuroscience
Volume 2016, Article ID 1715780, 8 pages
http://dx.doi.org/10.1155/2016/1715780
Research Article

A Feature Selection Approach Based on Interclass and Intraclass Relative Contributions of Terms

School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, Shaanxi 710048, China

Received 29 March 2016; Revised 21 June 2016; Accepted 11 July 2016

Academic Editor: Elio Masciari

Copyright © 2016 Hongfang Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Feature selection plays a critical role in text categorization. During feature selecting, high-frequency terms and the interclass and intraclass relative contributions of terms all have significant effects on classification results. So we put forward a feature selection approach, IIRCT, based on interclass and intraclass relative contributions of terms in the paper. In our proposed algorithm, three critical factors, which are term frequency and the interclass relative contribution and the intraclass relative contribution of terms, are all considered synthetically. Finally, experiments are made with the help of kNN classifier. And the corresponding results on 20 NewsGroup and SougouCS corpora show that IIRCT algorithm achieves better performance than DF, -Test, and CMFS algorithms.