An Experimental Study of the Joint Effects of Class Imbalance and Class Overlap
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-61816-1_9
Reference21 articles.
1. Shaukat, A.S., Usha, A.: An effective distance-based feature selection approach for imbalanced data. Appl. Intell. 50, 717–745 (2020)
2. Dai, Q., Liu, J.W., Shi, Y.H.: Class-overlap undersampling based on Schur decomposition for Class-imbalance problems. Expert Syst. Appl. 221, 119735 (2023)
3. Hoyos-Osorio, J., Alvarez-Meza, A., et al.: Relevant information undersampling to support imbalanced data classification. Neurocomputing 436, 136–146 (2021)
4. Li, D.-C., Wang, S.-Y., et al.: Learning class-imbalanced data with region-impurity synthetic minority oversampling technique. Inf. Sci. 607, 1391–1407 (2022)
5. Vuttipittayamongkol, P., Elyan, E., Petrovski, A.: On the class overlap problem in imbalanced data classification. Knowl.-Based Syst. 212, 106631(2021)
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