LIBRE: Learning Interpretable Boolean Rule Ensembles

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.