Scored Rule Sets for Interpretable Multiclass Classification
TL;DR
This paper analyzes structural relationships among established algorithms and identifies indications of research siloing, and provides a formal definition of scored rule sets, showing how they generalize classical rule-based models, and illustrating concrete transformations from logicGP, ExSTraCS, and CART representations.
Abstract
: Interpretable multiclass classification is studied across several research communities, but comparisons and conceptual integrations across rule sets, rule lists, decision trees, and evolutionary approaches are still limited. This paper addresses this gap by proposing scored rule sets as a unifying and interpretable hypothesis space. We first analyze structural relationships among established algorithms and identify indications of research siloing, especially between evolutionary and non-evolutionary lines of work. We then compare selected highly interpretable multiclass learners on UCI benchmark datasets. Beyond empirical comparison, we provide a formal definition of scored rule sets, show how they generalize classical rule-based models, and illustrate concrete transformations from logicGP, ExSTraCS, and CART representations. The resulting perspective clarifies when scored rule sets preserve interpretability while increasing modeling flexibility. Overall, the results support scored rule sets as a practically useful bridge between existing interpretable model families and as a basis for future cross-community method development.