Research on Application of Decision Tree in Classifying Data

With the rapid development of database technique, categorizing datasets becomes very important for discovering information. Decision tree classification provides a rapid and effective method of categorizing datasets. Although many algorithmic methods exist for optimizing decision tree structure, these can be vulnerable to changes in the training dataset. In this paper, an evolutionary method is presented, which allows decision tree flexibility through the use of co-evolving competition between the decision tree and the training data set. This method is validated via using one datasets. And the results indicate the utility of the proposed method in this paper is proved to be efficient in classifying Data.