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This research introduces CDPR, a novel algorithmic approach designed to generate interpretable IF-THEN rule sets for classification tasks by optimizing both discriminative power and coverage metrics. By leveraging submodular maximization techniques, the method achieves significant improvements in interpretability quality factors compared to existing state-of-the-art models that often sacrifice these attributes for raw accuracy. The work represents a meaningful step forward in balancing predictive performance with human-understandable logic within machine learning systems.
Learning High Coverage Discriminative Parsimonious Rulesets
arXiv:2606.14156v1 Announce Type: cross Abstract: Learning systems based on IF-THEN rule representations readily offer interpretability, making them a crucial focus in contemporary AI research. A key objective for such rule sets is to achieve both high discriminative power and interpretability. While existing state-of-the-art algorithms implicitly prioritize predictive accuracy, they often fall short on one or more quality metrics that ensure interpretability, such as coverage and parsimony of rule sets. Motivated by this, this paper propose the development of CDPR, which aims to create highly accurate and interpretable rule sets for classification problems. To the best of our knowledge, this represents the first attempt to establish such an approach. In this study, we introduce two algorithms rooted in submodular maximization, which not only provide provable guarantees on coverage but also yield rule sets that are both discriminative and parsimonious. We empirically demonstrate that rule sets learned through our approaches achieve higher accuracy and interpretability and has more than a 2.5-fold improvement in average coverage rates when compared to the next best algorithm.
Abstract:Learning systems based on IF-THEN rule representations readily offer interpretability, making them a crucial focus in contemporary AI research. A key objective for such rule sets is to achieve both high discriminative power and interpretability. While existing state-of-the-art algorithms implicitly prioritize predictive accuracy, they often fall short on one or more quality metrics that ensure interpretability, such as coverage and parsimony of rule sets. Motivated by this, this paper propose the development of CDPR, which aims to create highly accurate and interpretable rule sets for classification problems. To the best of our knowledge, this represents the first attempt to establish such an approach. In this study, we introduce two algorithms rooted in submodular maximization, which not only provide provable guarantees on coverage but also yield rule sets that are both discriminative and parsimonious. We empirically demonstrate that rule sets learned through our approaches achieve higher accuracy and interpretability and has more than a 2.5-fold improvement in average coverage rates when compared to the next best algorithm.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.14156 [cs.LG]
(or arXiv:2606.14156v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.14156 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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