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When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification?

paperactiveprovisionalwhen-does-synthetic-data-augmentation-improve-score-based-imbalanced-classification--4b1b033d·1 events·first seen Jun 25, 2026

Aliases: When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification?

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AUPRC AUROC

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4arXiv · cs.LG·Jun 25, 2026·source ↗

Theoretical framework for when synthetic data augmentation improves imbalanced classification metrics

A new arXiv preprint develops a theoretical framework characterizing when synthetic minority-class augmentation improves score-based metrics (AUROC, AUPRC, balanced accuracy, F1) under class imbalance. The authors show that under well-specified score models, augmentation provides no fundamental population-level improvement and may introduce bias, while under model misspecification it can correct ranking errors by shifting effective class balance. Minimax lower bounds confirm the raw estimator is already optimal in the well-specified regime, and simulation studies corroborate the theory.

Evaluation and Benchmarking AUPRC AUROC When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification?