A Scalable Two Stage Approach to Computing Optimal Decision Sets

TL;DR

This paper introduces a scalable two-stage method for learning minimum-size decision sets using rule enumeration and set cover optimization, improving on previous SAT-based approaches in terms of practicality and efficiency.

Contribution

The paper presents a novel two-stage approach that independently enumerates rules and then applies set cover optimization, enhancing scalability for decision set learning.

Findings

Outperforms existing SAT-based methods on various datasets.

Utilizes modern satisfiability and integer linear programming techniques.

Demonstrates practical scalability for large datasets.

Abstract

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.