LIBRE: Learning Interpretable Boolean Rule Ensembles

Graziano Mita, Paolo Papotti, Maurizio Filippone, Pietro Michiardi

Research output: Contribution to conferencePaperpeer-review

10 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages245-255
Number of pages11
StatePublished - 2020
Event23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020 - Virtual, Online
Duration: Aug 26 2020Aug 28 2020

Conference

Conference23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020
CityVirtual, Online
Period08/26/2008/28/20

Bibliographical note

Publisher Copyright:
Copyright © 2020 by the author(s)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

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