An improved SMOTE based on center offset factor and synthesis strategy for imbalanced data classification-Reference-Cited by-同舟云学术

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An improved SMOTE based on center offset factor and synthesis strategy for imbalanced data classification

Published:2024-06-23 Issue:15 Volume:80 Page:22479-22519
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Zhang Ying,Deng Li,Huang Hefeng,Wei Bo

Funder

Basic Public Welfare Research Program of Zhejiang Province

National Natural Science Foundation of China

Scientific Research Starting Foundation of Zhejiang Sci-Tech University

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11227-024-06287-3.pdf

Reference37 articles.

1. Tomašev N, Mladenić D (2013) Class imbalance and the curse of minority hubs. Knowl-Based Syst 53:157–172

2. Vasighizaker A, Jalili S (2018) C-PUGP: a cluster-based positive unlabeled learning method for disease gene prediction and prioritization. Comput Biol Chem 76:23–31

3. Jurgovsky J, Granitzer M, Ziegler K et al (2018) Sequence classification for credit-card fraud detection. Expert Syst Appl 100:234–245

4. Malhotra R, Kamal S (2019) An empirical study to investigate oversampling methods for improving software defect prediction using imbalanced data. Neurocomputing 343:120–140

5. Zhou X, Hu Y, Liang W et al (2020) Variational LSTM enhanced anomaly detection for industrial big data. IEEE Trans Ind Inf 17(5):3469–3477

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