Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Re-SSS: Rebalancing Imbalanced Data Using Safe Sample Screening

  • Shi, Hongbo (School of Information, Shanxi University of Finance and Economics) ;
  • Chen, Xin (School of Information, Shanxi University of Finance and Economics) ;
  • Guo, Min (School of Information, Shanxi University of Finance and Economics)
  • Received : 2019.12.26
  • Accepted : 2020.06.07
  • Published : 2021.02.28
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Abstract

Different samples can have different effects on learning support vector machine (SVM) classifiers. To rebalance an imbalanced dataset, it is reasonable to reduce non-informative samples and add informative samples for learning classifiers. Safe sample screening can identify a part of non-informative samples and retain informative samples. This study developed a resampling algorithm for Rebalancing imbalanced data using Safe Sample Screening (Re-SSS), which is composed of selecting Informative Samples (Re-SSS-IS) and rebalancing via a Weighted SMOTE (Re-SSS-WSMOTE). The Re-SSS-IS selects informative samples from the majority class, and determines a suitable regularization parameter for SVM, while the Re-SSS-WSMOTE generates informative minority samples. Both Re-SSS-IS and Re-SSS-WSMOTE are based on safe sampling screening. The experimental results show that Re-SSS can effectively improve the classification performance of imbalanced classification problems.

Keywords

Acknowledgement

This work is supported by the National Natural Science Foundation of China (No. 61801279), the Key Research and Development Project of Shanxi Province (No. 201903D121160), and the Natural Science Foundation of Shanxi Province (No. 201801D121115 and 201901D111318).

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