Learning High Coverage Discriminative Parsimonious Rulesets
For researchers and practitioners needing interpretable AI, this work addresses the gap in rule set quality metrics like coverage and parsimony, offering a principled approach with provable guarantees.
The paper introduces CDPR, a method for learning interpretable IF-THEN rule sets that achieve high accuracy and interpretability. The proposed algorithms, based on submodular maximization, yield rule sets with more than a 2.5-fold improvement in average coverage rates compared to the next best algorithm.
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.