A New ID3 Algorithm Based on Revised Information Gain

The ID3 algorithm is a decision tree algorithm,which is important in the field of machine learning.The concept of information gain is proposed by Quinlan in the ID3 algorithm.Information gain is the selection criteria of the best splitting attribute for inducing decision trees.This algorithm has some drawbacks,one of which is that it tends to choose multi-value attribute as the best splitting attribute.However,the multi-value attribute is not necessarily important for classification in the real world.This paper presents a revised information gain of the ID3 algorithm in an attempt to solve this problem.From the theoretical analysis and experimental results we can see that the new method has a good effect on multi-value orientation of the ID3 algorithm.