Cost-sensitive and hybrid-attribute measure multi-decision tree over imbalanced data sets

One of the most popular algorithms for classification is the decision tree. However, existing binary decision tree models do not handle well the minority class over imbalanced data sets. To address this difficulty, a Cost-sensitive and Hybrid attribute measure Multi-Decision Tree (CHMDT) approach is presented in this paper. It penalizes misclassification through a hybrid attribute measure, which is defined from the combination of the Gini index and information gain measure. It further builds a multi-decision tree consisting of multiple decision trees each with different root node information. The overall objective of the approach is to maximize the classification performance with the hybrid attribute measure while minimizing the total misclassification cost. Experiments are conducted over twelve KEEL imbalanced data sets to demonstrate the CHMDT approach. They show that the classification performance of the minority class is improved significantly without sacrifice of the overall classification accuracy of the majority class.