Improved ID3 Algorithm

as the classical algorithm of the decision tree classification algorithm, ID3 is famous for the merits of high classifying speed easy, strong learning ability and easy construction. But when use it to classify, there does exist the problem of inclining to chose attributions which has many values, which affects its practicality. This paper for solving the problem a decision tree algorithm based on attribute-importance is proposed. The improved algorithm uses attribute-importance to increase information gain of attribution which has fewer attributions and compares ID3 with improved ID3 by an example. The experimental analysis of the data show that the improved ID3 algorithm can get more reasonable and more effective rules.