An Pre-pruning ID3 Algorithm Based on the Mutual Information between Attributes
Chengyu Zhang · Journal of Guizhou University · 2008
ID3 algorithm is a popular and efficient heuristic algorithm in decision tree induction.This paper analyzes the shortcomings of the ID3 algorithm and proposes an extended version in which the testing attributes is selected based on not only the more mutual information between a candidate attribute and the class but also the less mutual information between a candidate attribute and the attribute of its ancestor nodes,in order to avoid selecting the redundant attributes and achieve the real reduce in entropy.And in the process of building tree,prune the tree with a pre-specified threshold,to avoiding the subset of instances is too small and loses the statistical significance of further divided.The experimental result indicates that it can construct a better decision tree compared with ID3.