Pandora's Regret is a closed-form pairwise scoring rule derived from expected optimal search costs that elicits true probabilities and outperforms log loss, accuracy, and F1 at predicting diagnostic costs on MedMNIST models.
Cost-sensitive learning for imbalanced medical data: a review , url =
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A hybrid framework uses adaptive bin partitioning, CVAE, multistage oversampling, LDWL loss, and gated fusion to improve performance on imbalanced regression benchmarks.
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Pandora's Regret: A Proper Scoring Rule for Evaluating Sequential Search
Pandora's Regret is a closed-form pairwise scoring rule derived from expected optimal search costs that elicits true probabilities and outperforms log loss, accuracy, and F1 at predicting diagnostic costs on MedMNIST models.
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Hybrid Imbalanced Regression Through Unified Data-Level and Algorithm-Level Balancing
A hybrid framework uses adaptive bin partitioning, CVAE, multistage oversampling, LDWL loss, and gated fusion to improve performance on imbalanced regression benchmarks.