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Framework for extreme imbalance classification: SWIM - sampling with the majority class.

, , , and . Knowl. Inf. Syst., 62 (3): 841-866 (2020)

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Understanding CNN Fragility When Learning With Imbalanced Data., , , , and . CoRR, (2022)Discovering co-location patterns with aggregated spatial transactions and dependency rules., , , and . Int. J. Data Sci. Anal., 5 (2-3): 137-154 (2018)ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry., , , , , , , , , and 3 other author(s). CoRR, (2023)RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification., , and . DSAA, page 1-2. IEEE, (2021)Time and temporal abstraction in continual learning: tradeoffs, analogies and regret in an active measuring setting., , , and . CoLLAs, volume 232 of Proceedings of Machine Learning Research, page 470-480. PMLR, (2023)Multi-label Classification of Anemia Patients., , , and . ICMLA, page 825-830. IEEE, (2015)Synthetic Oversampling with the Majority Class: A New Perspective on Handling Extreme Imbalance., , , , and . ICDM, page 447-456. IEEE Computer Society, (2018)Efficient Augmentation for Imbalanced Deep Learning., , , and . ICDE, page 1433-1446. IEEE, (2023)Learning when to observe: A frugal reinforcement learning framework for a high-cost world., , and . CoRR, (2023)Efficient Augmentation for Imbalanced Deep Learning., , , and . CoRR, (2022)