Article

Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System

Authors:

Michael Heider,

Helena Stegherr,

Jonathan Wurth,

Jörg HähnerAuthors Info & Claims

Bioinspired Optimization Methods and Their Applications: 10th International Conference, BIOMA 2022, Maribor, Slovenia, November 17–18, 2022, Proceedings

Pages 142 - 156

https://doi.org/10.1007/978-3-031-21094-5_11

Published: 17 November 2022 Publication History

Abstract

Achieving at least some level of explainability requires complex analyses for many machine learning systems, such as common black-box models. We recently proposed a new rule-based learning system, SupRB, to construct compact, interpretable and transparent models by utilizing separate optimizers for the model selection tasks concerning rule discovery and rule set composition. This allows users to specifically tailor their model structure to fulfil use-case specific explainability requirements. From an optimization perspective, this allows us to define clearer goals and we find that—in contrast to many state of the art systems—this allows us to keep rule fitnesses independent. In this paper we investigate this system’s performance thoroughly on a set of regression problems and compare it against XCSF, a prominent rule-based learning system. We find the overall results of SupRB’s evaluation comparable to XCSF’s while allowing easier control of model structure and showing a substantially smaller sensitivity to random seeds and data splits. This increased control can aid in subsequently providing explanations for both training and final structure of the model.

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Cited By

Siddique AHeider MIqbal MShiraishi HLi XHandl J(2024)A Survey on Learning Classifier Systems from 2022 to 2024Proceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664165(1797-1806)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664165
Shiraishi HHayamizu YHashiyama TSilva SPaquete L(2023)Fuzzy-UCS Revisited: Self-Adaptation of Rule Representations in Michigan-Style Learning Fuzzy-Classifier SystemsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590360(548-557)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590360
Heider MStegherr HPätzel DSraj RWurth JVolger BHähner J(2023)Discovering Rules for Rule-Based Machine Learning with the Help of Novelty SearchSN Computer Science10.1007/s42979-023-02198-x4:6Online publication date: 12-Oct-2023
https://dl.acm.org/doi/10.1007/s42979-023-02198-x

Index Terms

Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

Index terms have been assigned to the content through auto-classification.

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Discovering Rules for Rule-Based Machine Learning with the Help of Novelty Search
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SupRB in the context of rule-based machine learning methods: A comparative study
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Comprehending why an artificial intelligence–based agent makes certain predictions is often discussed as one of the central issues that needs to be solved in the near future to make such agents ubiquitously deployable. However, many common black-...
Highlights
- Detailed the presentation of SupRB, an interpretable machine learning algorithm.
- SupRB performs competitively on errors with generally smaller models.
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Information & Contributors

Information

Published In

cover image Guide Proceedings

Bioinspired Optimization Methods and Their Applications: 10th International Conference, BIOMA 2022, Maribor, Slovenia, November 17–18, 2022, Proceedings

Nov 2022

287 pages

ISBN:978-3-031-21093-8

DOI:10.1007/978-3-031-21094-5

Editors:
Marjan Mernik
University of Maribor, Maribor, Slovenia
,
Tome Eftimov
Jožef Stefan Institute, Ljubljana, Slovenia
,
Matej Črepinšek
University of Maribor, Maribor, Slovenia

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 17 November 2022

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Cited By

Siddique AHeider MIqbal MShiraishi HLi XHandl J(2024)A Survey on Learning Classifier Systems from 2022 to 2024Proceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664165(1797-1806)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664165
Shiraishi HHayamizu YHashiyama TSilva SPaquete L(2023)Fuzzy-UCS Revisited: Self-Adaptation of Rule Representations in Michigan-Style Learning Fuzzy-Classifier SystemsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590360(548-557)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590360
Heider MStegherr HPätzel DSraj RWurth JVolger BHähner J(2023)Discovering Rules for Rule-Based Machine Learning with the Help of Novelty SearchSN Computer Science10.1007/s42979-023-02198-x4:6Online publication date: 12-Oct-2023
https://dl.acm.org/doi/10.1007/s42979-023-02198-x

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