会议专题

Feature Selection Method Based on Category Correlation and Discernible Sets

  Feature selection method is very important for text categorization.In this paper,several classic feature selection methods are analyzed and their defficiencies are summarized firstly,and then a new feature selection method based on Category Correlation and Identification Set is presented.To implement the new presented selection method,a category correlation method combing document-frequency and word-frequency is proposed to filter out noise words and refine the feature space,and a attribute reduction algorithm based on discernible sets is applied to eliminate redundancies.By comparing the new presented selection method with classic feature selection methods in experimental results,it is found out that the presented feature selection method can obtain more representative feature subsets.

Feature Selection Category Correlation Rough set Discernible Set Attribute Reduction

Tong Sun Shen-Yi Qian Hao-Dong Zhu

School of Computer and Communication Engineering,ZhengZhou University of Light Industry,Henan Zhengzhou,450002,China

国内会议

2014全国理论计算机科学学术年会

济南

英文

1-12

2014-10-16(万方平台首次上网日期,不代表论文的发表时间)