A New Method to Construct Decision Trees form Training Instances

Abstract

In this paper, we present a new learning method to construct decision trees from training instances for handling classification problems. The decision trees constructed by the proposed method can be much smaller than the ones constructed by the ID3 algorithm. The proposed method is better than the ID3 algorithm when noise or coincidental regularities exist in the training instances.