Problem
Research questions and friction points this paper is trying to address.
synthetic data augmentation
imbalanced classification
score-based classification
class imbalance
distributional discrepancy
When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification?
🤖 AI Summary
This study investigates the mechanism by which synthetic data augmentation improves score-based classification performance—measured by metrics such as AUROC and AUPRC—in class-imbalanced settings. By developing a theoretical framework that disentangles the effects of augmentation on effective class weighting and distributional bias, and integrating tools from statistical learning theory, minimax analysis, and finite-sample error decomposition, the work establishes that under correctly specified models, augmentation solely reduces variance without improving overall performance. However, under model misspecification, it can mitigate ranking errors by correcting class imbalance. The analysis yields novel minimax lower bounds, which are corroborated through simulation experiments.
📝 Abstract
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-optimal for the metrics considered. Consequently, augmentation cannot provide a fundamental population-level improvement beyond possible finite-sample variance reduction, and may introduce additional bias through synthetic distributional error. We further establish minimax lower bounds showing that the raw estimator already achieves the optimal metric-regret rate in the well-specified regime. Under misspecification, however, augmentation can play a qualitatively different role: by changing the effective class balance, it can alter the restricted-class projection and correct ranking errors induced by the raw imbalanced objective. We provide explicit improvement bounds quantifying the roles of approximation error, finite-sample estimation error, and synthetic distributional error. Simulation studies corroborate the theory, demonstrating limited gains under well-specification and nontrivial but nonmonotone improvements under misspecification.
Innovation
Methods, ideas, or system contributions that make the work stand out.
synthetic data augmentation
class imbalance
score-based classification
model misspecification
minimax lower bounds
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