Noise-free sampling with majority framework for an imbalanced classification problem
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10115-024-02079-6.pdf
Reference64 articles.
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2. Rekha G, Tyagi AK, Krishna Reddy V (2020) A novel approach to solve class imbalance problem using noise filter method. In: Abraham A, Cherukuri AK, Melin P, Gandhi N (eds) Intelligent systems design and applications. Springer, Cham, pp 486–496. https://doi.org/10.1007/978-3-030-16657-1_45
3. Li J, Zhu Q, Wu Q, Fan Z (2021) A novel oversampling technique for class-imbalanced learning based on SMOTE and natural neighbors. Inf Sci 565:438–455. https://doi.org/10.1016/j.ins.2021.03.041
4. Guzmán-Ponce A, Sánchez JS, Valdovinos RM, Marcial-Romero JR (2021) DBIG-US: A two-stage under-sampling algorithm to face the class imbalance problem. Expert Syst Appl 168:114301. https://doi.org/10.1016/j.eswa.2020.114301
5. Rezvani S, Wang X (2023) A broad review on class imbalance learning techniques. Appl Soft Comput 143:110415. https://doi.org/10.1016/j.asoc.2023.110415
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