Decisions Tree Learning Method Based on Three-Way Decisions

Aiming at the problems that traditional data mining methods ignore inconsistent data, and general decision tree learning algorithms lack of theoretical support for the classification of inconsistent nodes. The three-way decision is introduced to decision tree learning algorithms, and the decision tree learning method based on three-way decisions is proposed. Firstly, the proportion of positive objects in node is used to compute the conditional probability of the three-way decision of node. Secondly, the nodes in decision tree arepartitioned to generate the three-way decision tree. The merger and pruning rules of the three-way decision tree are derived to convert the three-way decision tree into two-way decision tree by considering the information around nodes. Finally, an exampleisimplemented. The results show that the proposed method reserves inconsistent information, partitions inconsistent nodes by minimizing the overall risk, not only generates decision tree with cost-sensitivity, but also makes the partition of inconsistent nodes more explicable. Besides, the proposed method reduces the overfitting to some extent and the computation problem of conditional probability of three-way decisions is resolved.