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20 October 2022 CHS: a clustering-based hybrid sampling algorithm for the imbalanced classification
Shengwen Jia, Kai Shi
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Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124514I (2022) https://doi.org/10.1117/12.2656570
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China
Abstract
The traditional SVM has poor performance in imbalanced classification. To address the issue, a clustering-based hybrid sampling algorithm CHS is proposed in this paper. The majority samples are divided into different clusters by CFSFDP clustering algorithm for hierarchical undersampling. The minority samples are oversampled, and new minority samples are generated near the boundary based on the support vectors. Finally, the moth-flame optimization algorithm (MFO) is used to optimize the new minority samples in order to avoid the generation of noisy samples. The algorithm is applied to 16 KEEL datasets with different imbalance rates and compared with other sampling algorithms. The performance is evaluated by G-mean and AUC, and the experimental results show that CHS can effectively improve the performance of SVM for imbalanced classification.
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Shengwen Jia and Kai Shi "CHS: a clustering-based hybrid sampling algorithm for the imbalanced classification", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124514I (20 October 2022); https://doi.org/10.1117/12.2656570
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KEYWORDS
Optimization (mathematics)
Computer engineering
Computer science
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