Decision tree classification algorithm provides a fast and effective classification method for datasets, and it calculates information gain of each attribute, and selects the attribute with the greatest information gain as the split. However, to the best of our knowledge, when we use the traditional decision tree algorithm to analyze data in real life, we will encounter some unusual situations. This paper presents a new algorithm that can solve problems which majority voting algorithm can not be overcome, and verification of the accuracy of decision tree induction algorithm is improved.
展开▼
