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Class-Imbalance Learning Through the Lens of Class Priors and Data Distributions: A Structured Review of Intervention Points, Failure Mechanisms, and Method Selection

Class imbalance remains a central problem in machine learning because the rare class is often the event of scientific, clinical, financial, or operational interest. Yet the literature is fragmented across sampling, cost-sensitive learning, ensembles, representation learning, modern generative augmentation, and…