%0 Journal Article
%T Research on the Missing Attribute Value Data-oriented Decision Tree<br>面向属性值遗漏数据决策树分类算法研究
%A QIU Yun-fei
%A LI Xue
%A WANG Jian-kun
%A SHAO Liang-shan
%A <br>邱云飞
%A 李雪
%A 王建坤
%A 邵良杉
%J 计算机科学
%D 2011
%I 
%X In the existing multiple choice methods of decision trec'test attributes, can't sec such report as "I_et missing data processing integrated in the selection process of test attributes",however,the existing process methods of missing attribute value data could draw into bias in different degrees,based on this,proposed an information gain rate based on combination entropy as the decision tree's testing attributes selection criteria,which can eliminate missing value arrtib- utes'infulence on testing attributes selection,and carry out contrast experiments on WEKA. Experiment results indicate that the improvement can significantly increase whole efficiency and classification accuracy of the algorithm operation.
%K Missing attribute value data
%K Combination entropy
%K Decision tree<br>属性值遗漏数据，联合嫡，决策树
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=17DDCED190714E79D6427830823FDFAD&yid=9377ED8094509821&vid=16D8618C6164A3ED&iid=F3090AE9B60B7ED1&sid=0584DB487B4581F4&eid=5BC9492E1D772407&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=0