LIBRE: Learning Interpretable Boolean Rule Ensembles
This work addresses the need for interpretable machine learning models, particularly in domains where transparency is crucial, though it appears incremental as it builds on existing ensemble and rule-based approaches.
The authors tackled the problem of learning interpretable classifiers by introducing LIBRE, a method that produces Boolean rule ensembles, achieving competitive prediction accuracy with black box methods while offering superior interpretability.
We present a novel method - LIBRE - to learn an interpretable classifier, which materializes as a set of Boolean rules. LIBRE uses an ensemble of bottom-up weak learners operating on a random subset of features, which allows for the learning of rules that generalize well on unseen data even in imbalanced settings. Weak learners are combined with a simple union so that the final ensemble is also interpretable. Experimental results indicate that LIBRE efficiently strikes the right balance between prediction accuracy, which is competitive with black box methods, and interpretability, which is often superior to alternative methods from the literature.