%0 Journal Article
%T Collective CHI and IG feature selection method<br>集合CHI与IG的特征选择方法
%A WANG Guang
%A QIU Yun-fei
%A SHI Qing-wei
%A <br>王　光
%A 邱云飞
%A 史庆伟
%J 计算机应用研究
%D 2012
%I 
%X In order to make the selected features distribute intensively in a certain class and make features appear in that certain class as many as possible, this paper added the two adjusted parameters to the originally traditional CHI-square feature selection and IG feature selection method through analyzing the relevance between features and classes. Then it proposed a collective feature selection methodCCIFby combining with CHI and IG feature selection. Experiments show that CCIF improves the Micro-Pmore apparently by comparing with the traditional CHI and IG feature selection method.
%K text classification
%K feature weight
%K Chi-square statistic
%K information gain<br>文本分类
%K 特征选择
%K 卡方统计
%K 信息增益
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=3178011684088478834503762E73349D&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=DF92D298D3FF1E6E&sid=0AC9783FD6C67AB3&eid=48B2C66122C09ABA&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=10