%0 Journal Article
%T Improved C4.5 decision trees algorithm based on variable precision rough set<br>一种基于变精度粗糙集的C45决策树改进算法*
%A LIU Xing-wen
%A WANG Dian-hong
%A CHEN Fen-xiong
%A <br>刘兴文
%A 王典洪
%A 陈分雄
%J 计算机应用研究
%D 2011
%I 
%X Aiming at the problems of complexisity and relatively low classification accuracy of decision trees constructed by C4.5 algorithm, this paper proposed a new decision trees classification algorithm (VPRSC4.5) based on the variable precision rough set (VPRS), which took the approximate quality of classification as the heuristic function in order to alleviate the effect of noise data on choosing splitting attributes. It also gave out the solution to the problem how to choose the best attributes as the node when two or more attributes had the same value of approximate quality of classification. Experiments prove that the size and classification accuracy of the decision trees generated by the improved algorithm is superior to the C4.5 algorithm.
%K data mining
%K decision trees
%K information gain ratio
%K C4
%K 5 algorithm
%K rough set
%K variable precision rough set (VPRS)
%K approximate quality of classification<br>数据挖掘
%K 决策树
%K 信息增益率
%K C4.5算法
%K 粗糙集
%K 变精度粗糙集
%K 近似分类质量
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=1F8EB868F38CE072FE1A2323586722F1&yid=9377ED8094509821&vid=D3E34374A0D77D7F&iid=F3090AE9B60B7ED1&sid=B699C6A898F5C299&eid=B46CED1EB52E4972&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=14