RBSP-Boosting: A Shapley value-based resampling approach for imbalanced data classification

Abstract

Addressing the problem of imbalanced data category distribution in real applications and the problem of traditional classifiers tending to ensure the accuracy of the majority class while ignoring the accuracy of the minority class when processing imbalanced data, this paper proposes a method called RBSP-Boosting for imbalanced data classification. First, RBSP-Boosting introduces the Shapley value and calculates the Shapley value for each sample of the dataset through the truncated Monte Carlo method. Moreover, the proposed method removes the noise data according to the Shapley value and undersamples the samples with Shapley values less than zero in the majority class. Then, it takes the Shapley value as the weight of the sample and oversamples the minority class according to the weight. Finally, the new dataset is trained on the classifier through the AdaBoost classifier. Experiments are conducted on nine groups of UCI and KEEL datasets, and RBSP-Boosting is compared with four sampling algorithms: Random-OverSampler, SMOTE, Borderline-SMOTE and SVM-SMOTE. Experimental results show that the RBSP-Boosting method in the three evaluation metrics of AUC, F-score and G-mean, compared with the best performance of the four comparison algorithms, increases by 4.69%, 10.3% and 7.86%, respectively. The proposed method can significantly improve the effect of imbalanced data classification.