SAGE解决了欺诈检测中难以区分合法异常与真实欺诈的痛点，做风控或流媒体反作弊的团队可以直接参考其门控集成思路，值得一试。
音乐流媒体欺诈（如人为刷播放量）对平台和创作者构成威胁，但传统检测方法难以区分合法边缘案例（如超级粉丝、睡眠音乐会话）与欺诈行为。研究者提出SAGE方法，结合SimHash分层采样与模块化门控集成，从无标签数据中可靠识别负样本。该方法通过可配置统计门（马氏距离和k-NN密度）实现精度-召回率自适应权衡，并解决正-无标签学习中的表示偏差问题。在客户级和艺术家级欺诈检测任务上均表现优异，无需修改核心方法即可跨领域泛化。
SAGE: Scalable Automatic Gating Ensemble for Confident Negative Harvesting in Fraud Detection
Music streaming fraud, where bad actors artificially inflate stream counts to manipulate chart rankings and royalty payments, poses a significant threat to streaming services and legitimate content creators. Traditional fraud detection approaches struggle with a critical challenge: many legitimate edge cases, including super-fans and sleep-music sessions, exhibit activity patterns that closely mimic those of coordinated fraud. We present SAGE, a novel counterfactual-aware negative harvesting approach that combines SimHash-based stratified sampling with a modular gating ensemble for confident negative identification from unlabeled data. Our ensemble architecture employs pluggable statistical gates (currently instantiated with Mahalanobis distance and k-NN density) with configurable voting thresholds enabling adaptive precision-recall trade-offs. This addresses the representation bias problem in Positive-Unlabeled learning by ensuring comprehensive coverage of rare behavioral cohorts through floor-constrained sampling. Evaluation demonstrates strong precision and recall on held-out data. The approach generalizes across fraud detection domains, achieving strong performance on both customer-level and artist-level fraud without modification to the core methodology.