arXiv Machine Learning
Density-Ratio Rescoring for Imbalanced Classification
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Rethinking Class Imbalance for Single-Cell Foundation Models: A Systematic Benchmark Across Architectures and Long-Tail Loss Functions
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Beyond Rebalancing: Benchmarking Binary Classifiers Under Class Imbalance Without Rebalancing Techniques
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Beyond Logit Adjustment: A Residual Decomposition Framework for Long-Tailed Reranking
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The Hidden Cost of Resampling: How Imbalance Correction Degrades Probability Calibration in Tree Ensembles
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arXiv Machine Learning
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Hugging Face Trending Papers
Jun 24
When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification?
Synthetic data augmentation is widely used to mitigate class imbalance, but its theoretical effects on score-based classification remain poorly understood. This paper develops a framework for characterizing when synthetic minority augmentation can improve threshold-integrated and threshold-optimized metrics, including AUROC, AUPRC, best-threshold balanced accuracy, and best-threshold \(\F_1\) score.