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. We separate the effect of augmentation into two components: a change in effective class weighting and a discrepancy between the synthetic and true minority distributions. Under well-specified score models, the raw estimator already targets the likelihood-ratio ordering, which is population-opt...
| Authors | Zhengchi Ma, Pengfei Lyu, Anru R. Zhang |
|---|---|
| Primary category | stat.ML |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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