Decision Tree Inductive Learning Algorithm Based on Removing Noise Gradually

When noise exists in case base, high quality knowledge is hard to obtain by ID3 algorithm. For the weakness, by introducing the concept of second learning, the noisy data can be removed, which not only develop the decision tree, but also it can make good structure tree generate. So that we can abstract good rules information, and make the desirable tree more accurate. Especially, the more the data can be mined by decision tree algorithm, the better the efficiency and performance of the algorithm is, and the more obvious the superiority of algorithm is. This paper states the basic idea of algorithm, implementation process, performance analysis and accuracy proof in detail.