计算机工程与应用2024，Vol.60Issue(5)：165-171,7.DOI:10.3778/j.issn.1002-8331.2211-0413
利用可信反事实的不平衡数据过采样方法
Oversampling Method for Imbalanced Data Using Credible Counterfactual
摘要
Abstract
A new method for imbalanced data sets on counterfactual is proposed(counterfactual,CF),and further removes the"incredibility"composite 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 infor-mation 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.关键词
不平衡数据集/分类器/过采样/反事实(CF)Key words
imbalanced data/classifiers/oversampling/counterfactual(CF)分类
信息技术与安全科学引用本文复制引用
高峰,宋媚,祝义..利用可信反事实的不平衡数据过采样方法[J].计算机工程与应用,2024,60(5):165-171,7.基金项目
国家自然科学基金(No.62077029,71503108,61902161) （No.62077029,71503108,61902161）
江苏师范大学研究生科研创新项目(2022XKT1554). （2022XKT1554）
