用逻辑编程生成树模型的全局与局部解释规则
Generating Global and Local Explanations for Tree-Ensemble Learning Methods by Answer Set Programming
- 基于ASP构建规则集，利用决策树分裂结构生成解释
- 支持用户自定义约束，灵活生成可读性高的解释规则
- 适用于多种分类任务，帮助理解复杂树模型决策
我们提出一种基于答案集编程（ASP）的方法，为树集成学习方法生成全局和局部解释规则。通过分解式方法，利用基础决策树的分裂结构构造规则，并在ASP中编码模式挖掘方法以提取解释性规则。全局解释从整个训练好的树集成模型中选择候选规则；局部解释则仅考虑与特定预测实例相关的规则。我们展示了如何在ASP中声明式地表达用户定义的约束与偏好，实现透明且灵活的规则集生成。实验使用真实世界数据集和主流树集成算法验证了该方法的适用性，表明其能有效支持各类分类任务中的模型解释。
原文摘要 · Abstract (English)
We propose a method for generating rule sets as global and local explanations for tree-ensemble learning methods using Answer Set Programming (ASP). To this end, we adopt a decompositional approach where the split structures of the base decision trees are exploited in the construction of rules, which in turn are assessed using pattern mining methods encoded in ASP to extract explanatory rules. For global explanations, candidate rules are chosen from the entire trained tree-ensemble models, whereas for local explanations, candidate rules are selected by only considering rules that are relevant to the particular predicted instance. We show how user-defined constraints and preferences can be represented declaratively in ASP to allow for transparent and flexible rule set generation, and how rules can be used as explanations to help the user better understand the models. Experimental evaluation with real-world datasets and popular tree-ensemble algorithms demonstrates that our approach is applicable to a wide range of classification tasks. Under consideration in Theory and Practice of Logic Programming (TPLP).
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