Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by Answer Set Programming
This work addresses the need for transparent and flexible explanations in machine learning models, particularly for users seeking to understand complex tree-ensemble methods, though it appears incremental in combining existing techniques.
The paper tackled the problem of generating explainable rule sets from tree-ensemble learners by using Answer Set Programming (ASP) to exploit split structures and assess rules with pattern mining, demonstrating applicability across various real-world classification tasks.
We propose a method for generating explainable rule sets from tree-ensemble learners 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 interesting rules. 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.