Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by Answer Set Programming

TL;DR

This paper introduces a novel method that uses Answer Set Programming to generate transparent, explainable rule sets from tree-ensemble models, enhancing interpretability and user control.

Contribution

It presents a decompositional approach leveraging ASP to extract and assess rules from decision trees, allowing flexible, user-defined constraints for model explanations.

Findings

Applicable to various classification tasks

Effective in extracting interpretable rules

Supports user-defined constraints and preferences

Abstract

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.