用神经符号方法从表格数据中高效提取简洁高质量规则
Neurosymbolic Association Rule Mining from Tabular Data
- 通过欠完备自编码器生成数据的神经表示，挖掘特征关联
- 在5个数据集上优于7个基线，规则更少且覆盖完整数据
- 适合需要可解释性与高效推理的机器学习场景
关联规则挖掘（ARM）旨在以逻辑规则形式发现数据特征间的模式，应用广泛。然而高维数据常导致规则数量爆炸，增加计算时间并影响下游任务表现。为应对这一挑战，我们提出Aerial+，一种新型神经符号ARM方法。Aerial+利用欠完备自编码器生成数据的神经表示，捕捉特征间关联，并通过模型重建机制从中提取规则。在五个数据集上与七个基线对比的大量实验表明，Aerial+能学习更简洁、高质量的规则集，实现全数据覆盖，达到当前最优性能。将其集成到基于规则的可解释机器学习模型中，显著降低执行时间，同时保持或提升准确率。
原文摘要 · Abstract (English)
Association Rule Mining (ARM) is the task of mining patterns among data features in the form of logical rules, with applications across a myriad of domains. However, high-dimensional datasets often result in an excessive number of rules, increasing execution time and negatively impacting downstream task performance. Managing this rule explosion remains a central challenge in ARM research. To address this, we introduce Aerial+, a novel neurosymbolic ARM method. Aerial+ leverages an under-complete autoencoder to create a neural representation of the data, capturing associations between features. It extracts rules from this neural representation by exploiting the model's reconstruction mechanism. Extensive evaluations on five datasets against seven baselines demonstrate that Aerial+ achieves state-of-the-art results by learning more concise, high-quality rule sets with full data coverage. When integrated into rule-based interpretable machine learning models, Aerial+ significantly reduces execution time while maintaining or improving accuracy.
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