JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Re-SSS: Rebalancing Imbalanced Data Using Safe Sample Screening |
| 영문제목(English Title) |
Re-SSS: Rebalancing Imbalanced Data Using Safe Sample Screening |
| 저자(Author) |
Hongbo Shi
Xin Chen
Min Guo
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| 원문수록처(Citation) |
VOL 17 NO. 01 PP. 0089 ~ 0106 (2021. 02) |
한글내용
(Korean Abstract) |
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영문내용
(English 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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| 키워드(Keyword) |
Imbalanced Data
Safe Sample Screening
Re-SSS-IS
Re-SSS-WSMOTE
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| 파일첨부 |
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