  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.
