# LIBRE: Learning Interpretable Boolean Rule Ensembles

arXiv: https://arxiv.org/abs/1911.06537 · HTML: https://mlsanity.com/paper/1911.06537/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2019.

## Built on by (most cited first, 6 of 6)

- [Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach](https://mlsanity.com/paper/2206.03718/) (2022)
- [CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect](https://mlsanity.com/paper/2407.01004/) (2024)
- [Spectral Clustering of Categorical and Mixed-type Data via Extra Graph Nodes](https://mlsanity.com/paper/2403.05669/) (2024)
- [Personalized Interpretable Classification](https://mlsanity.com/paper/2302.02528/) (2023)
- [An Incremental MaxSAT-based Model to Learn Interpretable and Balanced Classification Rules](https://mlsanity.com/paper/2403.16418/) (2024)
- [Learning High Coverage Discriminative Parsimonious Rulesets](https://mlsanity.com/paper/2606.14156/) (2026)

## Abstract (arXiv)

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.