Improving ID3 Algorithm by Using A∗ Search

ID3 is one of the most widely used algorithms for creating a classification decision tree. However, the traditional ID3 algorithm has a difficulty when there are equally important attributes during the decision node construction. It randomly selects one of the most important attributes to serve as the current decision node. This behavior may lead to the resulting decision tree which contains unnecessary depths and suboptimal accuracy. The purpose of this paper is to find the near optimal depth decision tree of the dataset, which contains an equally important attributes problem, by using the A-Star (A∗) search. The experiment results on four standard datasets from UCI indicate that the proposed algorithm can significantly reduce the decision tree's depth and still maintain the classification accuracy.