Constructing Decision Tree by Integrating Multiple Information Metrics
Guanghua Chen, Zhengqun Wang, YU Zhen-zhou · 2009
In this paper, a new decision tree construction algorithm (MIDT) is proposed. MIDT (Multiple Informative Decision Tree) uses principal component analysis to integrate information gain, samples distribution information and correlation coefficient as the basis of the selection of splitting attributes. This method can overcome the disadvantage of ID3 decision tree construction method that uses information gain as the splitting attributes selection criteria as a result of its tendency to select the attribute with more values. And moreover, it can exert the complementarity between decision of entropy mean and decision of samples distribution.The results of experiments on the standard data sets provided by UCI show that the decision tree constructed by MIDT has higher classification accuracy and is more stable than ID3 and parametric estimation decision tree algorithm.