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Learning Locally Interpretable Rule Ensemble

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Machine Learning and Knowledge Discovery in Databases: Research Track (ECML PKDD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14171))

Abstract

This paper proposes a new framework for learning a rule ensemble model that is both accurate and interpretable. A rule ensemble is an interpretable model based on the linear combination of weighted rules. In practice, we often face the trade-off between the accuracy and interpretability of rule ensembles. That is, a rule ensemble needs to include a sufficiently large number of weighted rules to maintain its accuracy, which harms its interpretability for human users. To avoid this trade-off and learn an interpretable rule ensemble without degrading accuracy, we introduce a new concept of interpretability, named local interpretability, which is evaluated by the total number of rules necessary to express individual predictions made by the model, rather than to express the model itself. Then, we propose a regularizer that promotes local interpretability and develop an efficient algorithm for learning a rule ensemble with the proposed regularizer by coordinate descent with local search. Experimental results demonstrated that our method learns rule ensembles that can explain individual predictions with fewer rules than the existing methods, including RuleFit, while maintaining comparable accuracy.

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Notes

  1. 1.

    https://scikit-learn.org/stable/.

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Acknowledgement

We wish to thank Koji Maruhashi, Takuya Takagi, Ken Kobayashi, and Yuichi Ike for making a number of valuable suggestions. We also thank the anonymous reviewers for their insightful comments.

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Authors and Affiliations

  1. Artificial Intelligence Laboratory, Fujitsu Limited, Tokyo, Japan

    Kentaro Kanamori

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  1. Kentaro Kanamori
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Correspondence to Kentaro Kanamori .

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Editors and Affiliations

  1. University of Michigan, Ann Arbor, MI, USA

    Danai Koutra

  2. University of Vienna, Vienna, Austria

    Claudia Plant

  3. Max Planck Institute for Software Systems, Kaiserslautern, Germany

    Manuel Gomez Rodriguez

  4. Politecnico di Torino, Turin, Italy

    Elena Baralis

  5. CENTAI, Turin, Italy

    Francesco Bonchi

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Kanamori, K. (2023). Learning Locally Interpretable Rule Ensemble. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14171. Springer, Cham. https://doi.org/10.1007/978-3-031-43418-1_22

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  • DOI: https://doi.org/10.1007/978-3-031-43418-1_22

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  • Online ISBN: 978-3-031-43418-1

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