###
DOI:
Journal of Software:2008.19(1):82-89

基于区分类别能力的高性能特征选择方法
徐燕,李锦涛,王斌,孙春明
(中国科学院 计算技术研究所,北京 100080; 华北电力大学,北京 102206)
A Category Resolve Power-Based Feature Selection Method
XU Yan,LI Jin-Tao,WANG Bin,SUN Chun-Ming
()
Abstract
Chart / table
Reference
Similar Articles
Article :Browse 4877   Download 7426
Received:September 29, 2006    Revised:December 27, 2006
> 中文摘要: 特征选择在文本分类中起着重要作用.文档频率(document frequency,简称DF)、信息增益(information gain,简称IG)和互信息(mutual information,简称MI)等特征选择方法在文本分类中广泛应用.已有的实验结果表明,IG是最有效的特征选择算法之一,DF稍差,而MI效果相对较差.在文本分类中,现有的特征选择函数性能的评估均是通过实验验证的方法,即完全是基于经验的方法.特征选择是选择部分最有区分类别能力的特征,为此,给出了两个特征选择函数需满足的基本约束条件,并提
中文关键词: 特征选择  文本分类  信息检索
Abstract:One of the most important issues in Text Categorization (TC) is Feature Selection (FS). Many FS methods have been put forward and widely used in TC field, such as Information Gain (IG), Document Frequency (DF) thresholding, Mutual Information (MI) and so on. Empirical studies show that IG is one of the most effective methods, DF performs similarly, in contrast, and MI had relatively poor performance. One basic research question is why these FS methods cause different performance. Many existing work answers this question based on empirical studies. This paper presents a formal study of FS based on category resolve power. First, two desirable constraints that any reasonable FS function should satisfy are defined, then a universal method for developing FS functions is presented, and a new FS function KG using this method is developed. Analysis shows that IG and KG (knowledge gain) satisfy this universal method. Experiments on Reuters-21578 collection, NewsGroup collection and OHSUMED collection show that KG and IG get the best performance, even KG performs better than the IG method in two collections. These experiments imply that the universal method is very effective and gives a formal evaluation criterion for FS method.
文章编号:     中图分类号:    文献标志码:
基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.60473002, 60603094 (国家自然科学基金); the Beijing Natural Science Foundation of China under Grant No.4051004 (北京市自然科学基金) Supported by the National Natural Science Foundation of China under Grant Nos.60473002, 60603094 (国家自然科学基金); the Beijing Natural Science Foundation of China under Grant No.4051004 (北京市自然科学基金)
Foundation items:
Reference text:

徐 燕,李锦涛,王 斌,孙春明.基于区分类别能力的高性能特征选择方法.软件学报,2008,19(1):82-89

XU Yan,LI Jin-Tao,WANG Bin,SUN Chun-Ming.A Category Resolve Power-Based Feature Selection Method.Journal of Software,2008,19(1):82-89