AISTATS 2020poster25 citations
LIBRE: Learning Interpretable Boolean Rule Ensembles
Graziano Mita, Paolo Papotti, Maurizio Filippone, Pietro Michiardi
Abstract
We present a novel method—LIBRE—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.
BibTeX
@InProceedings{pmlr-v108-mita20a,
title = {LIBRE: Learning Interpretable Boolean Rule Ensembles},
author = {Mita, Graziano and Papotti, Paolo and Filippone, Maurizio and Michiardi, Pietro},
booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics},
pages = {245--255},
year = {2020},
editor = {Chiappa, Silvia and Calandra, Roberto},
volume = {108},
series = {Proceedings of Machine Learning Research},
month = {26--28 Aug},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v108/mita20a/mita20a.pdf},
url = {https://proceedings.mlr.press/v108/mita20a.html},
abstract = {We present a novel method—LIBRE—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.}
}