Class Imbalance Problem: A Wrapper-Based Approach using Under-Sampling with Ensemble Learning
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10796-024-10533-7.pdf
Reference81 articles.
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3. Beyan, C., & Fisher, R. (2015). Classifying imbalanced data sets using similarity based hierarchical decomposition. Pattern Recognition, 48(5), 1653–1672.
4. Bunkhumpornpat, C., Sinapiromsaran, K., & Lursinsap, C. (2009). Safe-Level-SMOTE: safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. In T. Theeramunkong, B. Kijsirikul, N. Cercone, & T. B. Ho (Eds.), Advances in knowledge discovery and data mining (vol. 5476). PAKDD 2009. Lecture Notes in Computer Science. Springer. https://doi.org/10.1007/978-3-642-01307-2_43
5. Bunkhumpornpat, C., & Sinapiromsaran, K. (2017). DBMUTE: Density-based majority under-sampling technique. Knowledge and Information Systems, 50(3), 827–850. https://doi.org/10.1007/s10115-016-0957-5
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