针对罕见病诊断的极端类别不平衡问题，提出高效增强与过滤方法。
Boundary-Aware Adversarial Filtering for Reliable Diagnosis under Extreme Class Imbalance
- 先合成少数类样本，再用对抗判别器和边界效用模型筛选
- 召回率和平均精度均优于SMOTE等主流方法，且校准性最佳
- 特别适合医疗诊断等漏诊代价高的场景
在极端类别不平衡场景下，如医疗诊断，召回率和校准性均至关重要。本文提出AF-SMOTE，一种数学驱动的增强框架：先合成少数类样本，再通过对抗判别器和边界效用模型进行过滤。在满足决策边界平滑性和类条件密度弱假设下，证明该过滤步骤单调提升F_beta（β≥1）的代理指标，且不增加Brier得分。在MIMIC-IV代理标签预测和典型欺诈检测基准上，AF-SMOTE的召回率和平均精度均高于SMOTE、ADASYN、Borderline-SMOTE、SVM-SMOTE等强基线，并实现最优校准。进一步验证了其在多个非MIMIC-IV数据集上的泛化优势。将AF-SMOTE应用于基于代理标签的医疗数据集，以疾病无关方式展示了其在临床中的实用价值——避免罕见病漏诊带来的严重后果。
原文摘要 · Abstract (English)
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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