关键词: 决策树, 冗余属性, 边界点, 阈值分割, 信息增益率

Abstract:

Aimed to solve the problem that the decision tree C4.5 algorithm has higher time complexity in the process of processing continuous value attributes, a new decision tree construction method is proposed. It uses the correlation coefficient between attributes（Pearson） in the probability set for the reduction of data-concentrated attributes, applies the information gain rate of the attribute and retains the optimal subset of the decision attribute to ensure that there is no redundant attribute in the attribute subset. What’s more, it also uses the decision of the boundary point to improve the threshold segmentation method in the discretization process of continuous value attributes and correct the calculation of the information gain rate. Using a data set in the UCI database, a series of comparative experiments are performed on the Pycharm platform. The results show that with the improved C4.5 decision tree algorithm, the decision tree generation efficiency is improved by about 50%, and the accuracy rate is improved by about 2%. The problem of the original C4.5 algorithm attribute selection partial continuous value attribute is solved.

Key words: decision tree, redundant attribute, boundary point, threshold segmentation, information gain rate