Research on the Missing Attribute Value Data-oriented Decision Tree
面向属性值遗漏数据决策树分类算法研究

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.