A Scalable Two Stage Approach to Computing Optimal Decision Sets
This work provides a more scalable method for generating interpretable rule-based models, which is crucial for practitioners in domains requiring explainable AI where privacy and safety are concerns.
This paper addresses the scalability limitations of existing SAT-based methods for generating minimum-size decision sets. It proposes a two-stage approach that first enumerates individual rules independently and then uses a set cover problem to select a subset of rules, demonstrating improved performance on various public datasets.
Machine learning (ML) is ubiquitous in modern life. Since it is being deployed in technologies that affect our privacy and safety, it is often crucial to understand the reasoning behind its decisions, warranting the need for explainable AI. Rule-based models, such as decision trees, decision lists, and decision sets, are conventionally deemed to be the most interpretable. Recent work uses propositional satisfiability (SAT) solving (and its optimization variants) to generate minimum-size decision sets. Motivated by limited practical scalability of these earlier methods, this paper proposes a novel approach to learn minimum-size decision sets by enumerating individual rules of the target decision set independently of each other, and then solving a set cover problem to select a subset of rules. The approach makes use of modern maximum satisfiability and integer linear programming technologies. Experiments on a wide range of publicly available datasets demonstrate the advantage of the new approach over the state of the art in SAT-based decision set learning.