An Ensemble Resampling Based Transfer AdaBoost Algorithm for Small Sample Credit Classification with Class Imbalance

Abstract

It is prone to overfitting and poor generalization ability for imbalanced small sample datasets in modeling. Auxiliary data is an effective solution. However, there may be data distribution differences between auxiliary data and small sample data, and the presence of noise samples affects the prediction performance. To address this issue, we propose an ensemble resampling based transfer AdaBoost (TrAdaBoost) algorithm for imbalanced small sample credit classification. The proposed algorithm framework has two stages: ensemble resampling dataset generation and weight adaptive transfer AdaBoost (WATrA) model prediction. In the first stage, neighborhood-based resampling technique is proposed to filter source data and reduce noise samples, followed by bagging resampling to balance the filtered source data. In the second stage, a weight adaptive TrAdaBoost model is utilized to address small sample with class imbalance issues and improve the effectiveness of the proposed method. We validate the proposed algorithm on two small sample credit datasets with class imbalance, and observe significant improvements in performance compared to traditional supervised machine learning methods and resampling methods based on the main evaluation criteria.