  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.
