%0 Journal Article
%T A Novel Feature Selection Heuristic Algorithm Based on Rough Set Theory<br>一种基于粗糙集启发式的特征选择算法
%A LIANG Yan
%A HE Zhong-Shi
%A <br>梁琰
%A 何中市
%J 计算机科学
%D 2007
%I 
%X In this paper, a new feature measurement RMI (Ratio of Mutual Information)is presented based on the concept of rough set theory about certain set and uncertain set. Then a novel heuristic algorithm, MRMI-UC (Algorithm based on Maximal Ratio of RMI and Uncertainty Coefficient), is proposed for Feature Selection based on rough set theory. Firstly, the Core is obtained by discernible matrix and formed as a candidate feature subset. With the starting point of Core, the rest features are filtered iteratively to maximize both RMI and Uncertainty Coefficient. Finally the algorithm is tested on the UCI datasets, experiment results show that MRMI-UC is feasible and can find a good feature subset in most cases.
%K Feature selection
%K Rough set theory
%K Heuristic algorithm
%K Uncertainty coefficient
%K Mutual information<br>特征选择
%K 粗糙集理论
%K 启发式算法
%K 不确定性系数
%K 互信息
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=95B64C826B494155229139AAB07473E1&yid=A732AF04DDA03BB3&vid=339D79302DF62549&iid=B31275AF3241DB2D&sid=F1177A9DF1349B63&eid=31611641D4BB139F&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=11