Research on the Missing Attribute Value Data-Oriented for Decision Tree

摘要：

In the existing multiple cboice methods of decision treetest attributes, cant see such report asLet missing data processing integrated in the selection process of test attributes,however,the existing process methods of missing attribute value data can draw into bias in different degrees,base on this,propose an information gain rate base on combination entropy as the decision trees testing attributes selection criteria,which can eliminate missing value arrtibutesinfulence on testing attributes selection,and be implemented on WEKA.The computational complexity of the MultilnfoTree is better than that of C4.5.

关键词： missing attribute value data combination entropy decision tree

作者: Qiu Yun-fei Zhang Xin-yan Li Xue Shao Liang-shan

作者单位: Institute of System Engineering Liaoning Technical University Huludao 125100, China

会议类型: 国际会议

会议名称: 2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)

会议地点: 大连

会议语种:英文

页码: 1477-1479

在线出版日期: 2010-07-05（万方平台首次上网日期，不代表论文的发表时间）