On the Learning of Explainable Classification Rules through Disjunctive Patterns

Explainability is a fundamental principle in the field of Artificial Intelligence (AI), ensuring that AI models and systems are understandable and transparent to end-users. Specifically, it tackles the challenge of providing explanations for AI predictions. In interpretable machine learning, classification rules are regarded one of the most well-known explainability techniques, due to their expressive power and transparent structure. In this paper, we first show that computing classification rules is equivalent to mining disjunctive patterns from the corresponding transaction database. Second, we show that our approach provides a clear characterization of optimal classification rules, wherein disjunctive patterns satisfy the non-redundancy property in the target class and such patterns correspond to minimal generators in this class. Then, we propose a SAT-based solution of the problem for computing optimal classification rules using MaxSAT solvers, for which the optimality is a balancing between the accuracy and the size of the rules. Finally, we present an empirical evaluation on several representative datasets, showing that our approach achieves good performance in terms of accuracy and interpretability compared to existing baselines.