A novel method of constructing the cost-sensitive decision trees based on multi-objective optimization is proposed in this paper.The average misclassification cost and the average test cost are treated as the two optimization objectives.NNIA (Nondominated Neighbor Immune Algorithm) is exploited to optimize the decision trees.And some Pareto decision trees are finally obtained.Experimental results show that

compared with the C4.5 algorithm and CSDB (Cost Sensitive Decision Tree) algorithm

the proposed method in this paper can not only outperform these two methods in terms of the two above objectives but also achieve smaller size of the decision trees and stronger generalization ability.