Abstract: It often comes with imbalanced data problem when using classification models in the real world applications, such as credit risk prediction and medical diagnosis. In these applications, it is important to improve the accuracy over the minority class, so the performance on the TPR (True Positive Rate) is significant. SMOTEBagging has a better TPR than the normal Bagging model. In order to further improve the TPR of SMOTEBagging, the AHP method is used to selectively integrate the base classifiers and get a novel model, named AHP-Based Bagging. The experimental results show that AHP-Based Bagging can get a better TPR with smaller ensemble size, and not to sacrifice the overall performance, which is more practical.