Boundary-Aware Adversarial Filtering for Reliable Diagnosis under Extreme Class Imbalance

• URL: http://arxiv.org/abs/2511.17629v1
• Date: Wed, 19 Nov 2025 02:15:58 GMT
• Title: Boundary-Aware Adversarial Filtering for Reliable Diagnosis under Extreme Class Imbalance
• Authors: Yanxuan Yu, Michael S. Hughes, Julien Lee, Jiacheng Zhou, Andrew F. Laine,
• Abstract summary: We propose AF-SMOTE, a mathematically motivated augmentation framework that first synthesizes minority points and then filters them by an adversarial discriminator and a boundary utility model.<n>We prove that, under mild assumptions on the decision boundary smoothness and class-conditional densities, our filtering step monotonically improves a surrogate of F_beta.<n>On MIMIC-IV proxy label prediction and canonical fraud detection benchmarks, AF-SMOTE attains higher recall and average precision than strong oversampling baselines.
• Score: 1.2948544197525087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study classification under extreme class imbalance where recall and calibration are both critical, for example in medical diagnosis scenarios. We propose AF-SMOTE, a mathematically motivated augmentation framework that first synthesizes minority points and then filters them by an adversarial discriminator and a boundary utility model. We prove that, under mild assumptions on the decision boundary smoothness and class-conditional densities, our filtering step monotonically improves a surrogate of F_beta (for beta >= 1) while not inflating Brier score. On MIMIC-IV proxy label prediction and canonical fraud detection benchmarks, AF-SMOTE attains higher recall and average precision than strong oversampling baselines (SMOTE, ADASYN, Borderline-SMOTE, SVM-SMOTE), and yields the best calibration. We further validate these gains across multiple additional datasets beyond MIMIC-IV. Our successful application of AF-SMOTE to a healthcare dataset using a proxy label demonstrates in a disease-agnostic way its practical value in clinical situations, where missing true positive cases in rare diseases can have severe consequences.

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