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Usefulness of Quantile-Aware Diffusion Modeling for Highly Imbalanced Tabular Data

Talha, Abu et al.
arXiv (All) · Papers
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applicationsmachine-learning
Classification problem in the context of highly imbalanced data is a major challenge in many real-world applications (e.g., FinTech, healthcare, etc.). In these cases, the vast majority of instances belong to a single class and a small fraction represent the minority class (often the most critical c
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