Research on algorithm of decision tree induction

This paper introduces the development trend of decision trees in the past several years. During these years, the ID3 algorithm has been at its highest point in decision trees. ID3 makes use of information entropy as heuristics to select a "excellent attribution" at each node, so it can get a smaller depth but not the proper width of the tree, i.e., if the width is large, no matter how small the decision tree is, the leaf node will not be small. It is very important that smaller nodes get higher classify precision for decision trees. This paper provides two algorithms, which can avoid the deficiency of ID3 and reduce the width of the tree to get a better result - the one is probability based PID, and the other is entropy based EMID.