An improved Decision Tree classification algorithm based on ID3 and the application in score analysis

The Decision Tree is an important classification method in data mining classification. Aiming at deficiency of ID3 algorism, a new improved classification algorism is proposed in this paper. The new algorithm combines principle of Taylor Formula with information entropy solution of ID3 algorism, and simplifies the information entropy solution of ID3 algorithm, then assigns a weight value N to simplified information entropy. It avoids deficiency of ID3 algorism which is apt to sample much value for testing. The improved algorithm is applied in score analysis and analyzed through experiment. The experiment results show that simplified entropy weight algorism spends decrease 65 Seconds compares ID3 algorithm in building up decision tree, and the accuracy was increased by 3%.