Oversampling Method for Imbalanced Data Using Credible Counterfactual

A new method for imbalanced data sets on counterfactual is proposed (counterfactual，CF), and further removes the incredibilitycomposite samples, which aims to solve the problem of the traditional sampling method that cannot make full use of the data set information. Its core idea is to synthesize new samples based on the original instance features of the dataset. Compared with the traditional oversampling interpolation method, it can fully mine the boundary decision information in the data, so as to provide more useful information for the classifier and improve the classification performance. A lot of comparative experiments have been carried out on 9 KEEL and UCI unbalanced datasets, 5 different classifiers (SVM, DT, Logistic, RF, AdaBoost) and 4 traditional oversampling methods (SMOTE, B1- SMOTE, B2- SMOTE, ADASYN). The results show that the algorithm has higher AUC value、F1 value and G-mean value, which can effectively solve the class imbalance problem. © 2024 Editorial Department of Scientia Agricultura Sinica. All rights reserved.