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On the efficient implementation of classification rule learning

Author

Listed:
  • Michael Rapp

    (Ludwig-Maximilians-Universität München)

  • Johannes Fürnkranz

    (Johannes Kepler University Linz)

  • Eyke Hüllermeier

    (Ludwig-Maximilians-Universität München)

Abstract

Rule learning methods have a long history of active research in the machine learning community. They are not only a common choice in applications that demand human-interpretable classification models but have also been shown to achieve state-of-the-art performance when used in ensemble methods. Unfortunately, only little information can be found in the literature about the various implementation details that are crucial for the efficient induction of rule-based models. This work provides a detailed discussion of algorithmic concepts and approximations that enable applying rule learning techniques to large amounts of data. To demonstrate the advantages and limitations of these individual concepts in a series of experiments, we rely on BOOMER—a flexible and publicly available implementation for the efficient induction of gradient boosted single- or multi-label classification rules.

Suggested Citation

  • Michael Rapp & Johannes Fürnkranz & Eyke Hüllermeier, 2024. "On the efficient implementation of classification rule learning," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(4), pages 851-892, December.
    • Handle: RePEc:spr:advdac:v:18:y:2024:i:4:d:10.1007_s11634-023-00553-7
    DOI: 10.1007/s11634-023-00553-7
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    References listed on IDEAS

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    1. Sarah Friedrich & Gerd Antes & Sigrid Behr & Harald Binder & Werner Brannath & Florian Dumpert & Katja Ickstadt & Hans A. Kestler & Johannes Lederer & Heinz Leitgöb & Markus Pauly & Ansgar Steland & A, 2022. "Is there a role for statistics in artificial intelligence?," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(4), pages 823-846, December.
    2. David Conde & Miguel A. Fernández & Cristina Rueda & Bonifacio Salvador, 2021. "Isotonic boosting classification rules," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(2), pages 289-313, June.
    3. Stanislav Vojíř & Tomáš Kliegr, 2020. "Editable machine learning models? A rule-based framework for user studies of explainability," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 785-799, December.
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