title: A feature selection approach based on term distributions.
abstract: Feature selection has a direct impact on text categorization. Most existing algorithms are based on document level, and they haven't considered the influence of term frequency on text categorization. Based on these, we put forward a feature selection approach, FSATD, based on term distributions in the paper. In our proposed algorithm, three critical factors which are term frequency, the inter-class distribution and the intra-class distribution of the terms are all considered synthetically. Finally, experiments are made with the help of kNN classifier. And the corresponding results on 20NewsGroup and SougouCS corpus show that FSATD algorithm achieves better performance than DF and t-Test algorithms.
authors.affiliation: School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, 710048 Shaanxi China.
authors.forename: Hongfang
authors.initials: H
authors.lastname: Zhou
authors.affiliation: School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, 710048 Shaanxi China.
authors.forename: Jie
authors.initials: J
authors.lastname: Guo
authors.affiliation: School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, 710048 Shaanxi China.
authors.forename: Yinghui
authors.initials: Y
authors.lastname: Wang
keywords: Feature selection; Term distributions; Term frequency; Text categorization