%0 Journal Article
%T New classifier ensemble method based on rough set attribute reduction<br>一种基于粗糙集属性约简的多分类器集成方法
%A YANG Chuan-zhen
%A ZHU Yu-quan
%A CHEN Geng
%A <br>杨传振
%A 朱玉全
%A 陈耿
%J 计算机应用研究
%D 2012
%I 
%X To improve the accuracy of multiple classifier system, this paper proposed an classifier ensemble method MCS_ARS. This method used rough set attribute reduction and data partition to obtain a number of features subset and data subset to train base classifier, then it used the similarity of the classification results to get the results of validation set and got the final classification results of validation set by majority voting. Experiment results on UCI data sets show that compared to classical ensemble methods, MCS_ARS has higher classification accuracy and stability.
%K ensemble learning
%K rough set
%K attribute reduction<br>集成学习
%K 粗糙集
%K 属性约简
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=F886A295C1E57CC50EDB11FBC8ED8BB8&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=94C357A881DFC066&sid=F8C186D6055F60DE&eid=6F76CBC90E48A1BC&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=11