Alper unifies entity resolution matching and clustering into an iterative graph refinement and probabilistic label propagation process that adaptively selects LLM queries via a budgeted greedy optimization to outperform cascaded pipelines on eight benchmarks.
A Comprehensive Survey of Data Mining-based Fraud Detection Research
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abstract
This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries. Within the business context of mining the data to achieve higher cost savings, this research presents methods and techniques together with their problems. Compared to all related reviews on fraud detection, this survey covers much more technical articles and is the only one, to the best of our knowledge, which proposes alternative data and solutions from related domains.
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Encoder-decoder model detects synthetic anomalies in additive manufacturing image sequences unsupervised and surfaces previously unnoticed temperature non-uniformity.
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Adaptive Graph Refinement and Label Propagation with LLMs for Cost-Effective Entity Resolution
Alper unifies entity resolution matching and clustering into an iterative graph refinement and probabilistic label propagation process that adaptively selects LLM queries via a budgeted greedy optimization to outperform cascaded pipelines on eight benchmarks.