Re-SSS: Rebalancing Imbalanced Data Using Safe Sample Screening


Hongbo Shi, Xin Chen, Min Guo, Journal of Information Processing Systems Vol. 17, No. 1, pp. 89-106, Feb. 2021  

10.3745/JIPS.01.0065
Keywords: Imbalanced Data, Safe Sample Screening, Re-SSS-IS, Re-SSS-WSMOTE
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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.


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Cite this article
[APA Style]
Hongbo Shi, Xin Chen, & Min Guo (2021). Re-SSS: Rebalancing Imbalanced Data Using Safe Sample Screening. Journal of Information Processing Systems, 17(1), 89-106. DOI: 10.3745/JIPS.01.0065.

[IEEE Style]
H. Shi, X. Chen and M. Guo, "Re-SSS: Rebalancing Imbalanced Data Using Safe Sample Screening," Journal of Information Processing Systems, vol. 17, no. 1, pp. 89-106, 2021. DOI: 10.3745/JIPS.01.0065.

[ACM Style]
Hongbo Shi, Xin Chen, and Min Guo. 2021. Re-SSS: Rebalancing Imbalanced Data Using Safe Sample Screening. Journal of Information Processing Systems, 17, 1, (2021), 89-106. DOI: 10.3745/JIPS.01.0065.