%0 Journal Article
%T Ant colony feature selection based on fuzzy rough set information entropy<br>基于模糊粗糙集信息熵的蚁群特征选择方法
%A ZHAO Jun-yang
%A ZHANG Zhi-li
%A <br>赵军阳
%A 张志利
%J 计算机应用
%D 2009
%I 
%X Most heuristic feature selection algorithms converge easily to local-best, which cannot search the whole feature space effectively. In order to improve the parallel search ability to feature space, the information entropy theory of fuzzy rough set was introduced to ant colony model, and the ant search strategy, pheromone updating and state transition rules of the model have been modified to realize ant colony model based feature selection. UCI datasets experiments indicate that the proposed algorithm is effective to feature subset selection compared with three classical feature selection algorithms.
%K Feature Selection
%K Ant Colony Algorithm (ACA)
%K Fuzzy Rough Set
%K Information Entropy<br>特征选择
%K 蚁群算法
%K 模糊粗糙集
%K 信息熵
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=43C502A4CE23FA03D240D8BE5F360D36&yid=DE12191FBD62783C&vid=771469D9D58C34FF&iid=CA4FD0336C81A37A&sid=91C9056D8E8856E0&eid=5CFEDA028F0B4483&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=10