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AntTech
AntTech
Nov 6, 2022 · Artificial Intelligence

Advanced Rule Learning, Constraint‑Adaptive Frameworks, and Semi‑Supervised Data Augmentation for Fraud Detection and Imbalanced Ranking

This article surveys recent Ant Group research on explainable fraud detection, including constraint‑adaptive rule‑set learning (CRSL), meta‑path guided rule generation (MetaRule), biased sampling for imbalanced ranking, and a semi‑supervised data‑augmentation framework (SDAT) for tabular data, highlighting their motivations, methodologies, deployments, and experimental results.

AI researchGraph Neural NetworksSemi-supervised Learning
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Advanced Rule Learning, Constraint‑Adaptive Frameworks, and Semi‑Supervised Data Augmentation for Fraud Detection and Imbalanced Ranking