用可微归纳逻辑编程提升欺诈检测可解释性，适合规则挖掘场景。
Differentiable Inductive Logic Programming for Fraud Detection
- 将可微归纳逻辑编程用于欺诈检测，通过数据清洗适配背景事实格式。
- 在传统决策树与深度符号分类方法中表现相当，未显著提升性能。
- 擅长递归规则学习，适合需要可解释规则的欺诈分析场景。
当前机器学习趋势更重视可解释性，即使以性能为代价。因此，可解释人工智能方法在欺诈检测领域尤为重要。本文研究可微归纳逻辑编程（DILP）作为欺诈检测中可解释AI方法的适用性。尽管DILP的可扩展性是公认问题，但通过数据清洗和将表格及数值数据调整为背景事实语句的预期格式，其应用性显著提高。虽然在处理过程中未表现出对决策树或深度符号分类等主流方法的明显优势，但仍能取得相当的结果。本文揭示了其局限性并提出改进方向，同时指出其在递归规则学习等场景下相较于传统方法更具潜力的应用价值。
原文摘要 · Abstract (English)
Current trends in Machine Learning prefer explainability even when it comes at the cost of performance. Therefore, explainable AI methods are particularly important in the field of Fraud Detection. This work investigates the applicability of Differentiable Inductive Logic Programming (DILP) as an explainable AI approach to Fraud Detection. Although the scalability of DILP is a well-known issue, we show that with some data curation such as cleaning and adjusting the tabular and numerical data to the expected format of background facts statements, it becomes much more applicable. While in processing it does not provide any significant advantage on rather more traditional methods such as Decision Trees, or more recent ones like Deep Symbolic Classification, it still gives comparable results. We showcase its limitations and points to improve, as well as potential use cases where it can be much more useful compared to traditional methods, such as recursive rule learning.
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