arXiv:2606.26053v1 Announce Type: cross 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
The proliferation of AI applications across various sectors, coupled with the inherent issues of imbalanced datasets in real-world scenarios, necessitates robust theoretical understanding to optimize model performance.
This research provides a foundational framework to understand and improve AI model reliability and fairness, particularly in critical applications where data imbalance can lead to biased or poor decision-making.
The theoretical framework establishes clearer guidelines for the effective application of synthetic data augmentation, potentially leading to more deliberate and successful use in AI development.
- · AI developers
- · Data scientists
- · Sectors with imbalanced datasets (e.g., finance, healthcare)
- · Trustworthy AI initiatives
- · Organizations relying on ad-hoc augmentation without theoretical grounding
- · Ineffective or poorly optimized AI models
Improved performance and reliability of AI systems handling imbalanced data.
Reduced deployment risks for AI in sensitive applications due to better-understood data augmentation strategies.
Acceleration of AI adoption in fields previously hampered by data quality and bias concerns, especially within agentic systems.
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Read at arXiv cs.LG