Selective oversampling approach for strongly imbalanced data

作     者：Gnip, Peter Vokorokos, Liberios Drotar, Peter 

作者机构：Tech Univ Kosice Dept Comp & Informat Kosice Slovakia 

出 版 物：《PEERJ COMPUTER SCIENCE》 (PeerJ Comput. Sci.)

年 卷 期：2021年第7卷

页      面：e604-e604页

核心收录：

基　　金：Slovak Research and Development Agency [APVV-16-0211] Ministry of Education, Science, Research and Sport of the Slovak Republic VEGA [1/0327/20] 

主　　题：Imbalanced data Oversampling Outlier detection SMOTE ADASYN Bankruptcy prediction 

摘      要：Challenges posed by imbalanced data are encountered in many real-world applications. One of the possible approaches to improve the classifier performance on imbalanced data is oversampling. In this paper, we propose the new selective oversampling approach (SOA) that first isolates the most representative samples from minority classes by using an outlier detection technique and then utilizes these samples for synthetic oversampling. We show that the proposed approach improves the performance of two state-of-the-art oversampling methods, namely, the synthetic minority oversampling technique and adaptive synthetic sampling. The prediction performance is evaluated on four synthetic datasets and four real-world datasets, and the proposed SOA methods always achieved the same or better performance than other considered existing oversampling methods.