RUBRIC: Realism--Utility Balanced Ranking for Imbalanced Classification

• URL: http://arxiv.org/abs/2607.09816v1
• Date: Fri, 10 Jul 2026 06:37:12 GMT
• Title: RUBRIC: Realism--Utility Balanced Ranking for Imbalanced Classification
• Abstract summary: Class imbalance poses a fundamental challenge in risk-sensitive applications such as fraud detection and medical diagnosis.<n>We introduce RUBRIC, a generator-agnostic filtering framework that formulates synthetic sample selection as a quality-over-quantity optimization problem.<n>We show that RUBRIC improves F1-macro and recall while maintaining comparable ROC-AUC across several generators.
• Score: 38.8278639079174
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Class imbalance poses a fundamental challenge in risk-sensitive applications such as fraud detection and medical diagnosis, where minority-class samples are scarce yet critical for accurate classification. Existing oversampling methods generate synthetic samples to rebalance class distributions; however, they often produce large numbers of low-quality candidates that distort decision boundaries or introduce artifacts, leading to overfitting and degraded generalization. In this work, we introduce RUBRIC, a generator-agnostic filtering framework that formulates synthetic sample selection as a quality-over-quantity optimization problem. RUBRIC ranks candidates using a realism-utility trade-off: realism is quantified by a learned discriminator that distinguishes real samples from synthetic samples, while utility captures proximity to the decision boundary through a concave margin-based scoring function. We show that, under mild regularity conditions, the proposed filtering strategy monotonically tightens the generalization bound for margin-based classifiers by jointly reducing distribution shift and suppressing near-negative tail contributions. Through extensive experiments on credit-card fraud detection and other imbalanced benchmarks, we demonstrate that RUBRIC improves F1-macro and recall while maintaining comparable ROC-AUC across several generators. We also provide explicit lambda-sensitivity analysis to show how users can recover AUPRC when ranking quality is prioritized.

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