LIBRE: Learning Interpretable Boolean Rule Ensembles

Graziano Mita, Paolo Papotti, Maurizio Filippone, Pietro Michiardi
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:245-255, 2020.

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.

Cite this Paper

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.} }
Endnote
%0 Conference Paper %T LIBRE: Learning Interpretable Boolean Rule Ensembles %A Graziano Mita %A Paolo Papotti %A Maurizio Filippone %A Pietro Michiardi %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-mita20a %I PMLR %P 245--255 %U https://proceedings.mlr.press/v108/mita20a.html %V 108 %X 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.
APA
Mita, G., Papotti, P., Filippone, M. & Michiardi, P.. (2020). LIBRE: Learning Interpretable Boolean Rule Ensembles. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:245-255 Available from https://proceedings.mlr.press/v108/mita20a.html.

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