用提升机模型缩小搜索空间，让贝叶斯网络结构学习更快更准。
Scalable Bayesian Network Structure Learning Using Tsetlin Machine to Constrain the Search Space
- 用提升机提取关键变量，只对精选变量做条件独立检验
- 在Munin1、Hepar2数据集上速度提升显著，准确率与主流方法相当
- 适合处理大规模数据的因果发现，兼顾效率与精度
PC算法是因果推断中学习贝叶斯网络结构的常用方法，但其时间复杂度随数据规模增长而急剧上升，限制了在大规模真实场景中的应用。本文提出一种新方法，利用提升机（Tsetlin Machine, TM）提取最具代表性的逻辑项，并仅对这些筛选出的变量进行条件独立性（CI）测试，而非全变量集，从而大幅降低计算开销。我们在bnlearn数据库中的多组分类数据集（如Munin1、Hepar2）上实现并评估该方法，结果表明，基于提升机的方法不仅显著降低计算复杂度，且在因果发现任务中保持与当前先进方法相当的准确性，为传统PC算法提供了高效可行的替代方案。
原文摘要 · Abstract (English)
The PC algorithm is a widely used method in causal inference for learning the structure of Bayesian networks. Despite its popularity, the PC algorithm suffers from significant time complexity, particularly as the size of the dataset increases, which limits its applicability in large-scale real-world problems. In this study, we propose a novel approach that utilises the Tsetlin Machine (TM) to construct Bayesian structures more efficiently. Our method leverages the most significant literals extracted from the TM and performs conditional independence (CI) tests on these selected literals instead of the full set of variables, resulting in a considerable reduction in computational time. We implemented our approach and compared it with various state-of-the-art methods. Our evaluation includes categorical datasets from the bnlearn repository, such as Munin1, Hepar2. The findings indicate that the proposed TM-based method not only reduces computational complexity but also maintains competitive accuracy in causal discovery, making it a viable alternative to traditional PC algorithm implementations by offering improved efficiency without compromising performance.
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