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

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Bioinspired Optimization Methods and Their Applications (BIOMA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13627))

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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Notes

  1. 1.

    https://github.com/rpreen/xcsf, https://doi.org/10.5281/zenodo.5806708.

  2. 2.

    https://github.com/heidmic/suprb, https://doi.org/10.5281/zenodo.6460701.

  3. 3.

    https://github.com/rpreen/xcsf/wiki/Python-Library-Usage.

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Authors and Affiliations

  1. Universiät Augsburg, Am Technologiezentrum 8, Augsburg, Germany

    Michael Heider, Helena Stegherr, Jonathan Wurth, Roman Sraj & Jörg Hähner

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  1. Michael Heider
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  2. Helena Stegherr
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  3. Jonathan Wurth
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  4. Roman Sraj
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  5. Jörg Hähner
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Correspondence to Michael Heider .

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Editors and Affiliations

  1. University of Maribor, Maribor, Slovenia

    Marjan Mernik

  2. Jožef Stefan Institute, Ljubljana, Slovenia

    Tome Eftimov

  3. University of Maribor, Maribor, Slovenia

    Matej Črepinšek

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Heider, M., Stegherr, H., Wurth, J., Sraj, R., Hähner, J. (2022). Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System. In: Mernik, M., Eftimov, T., Črepinšek, M. (eds) Bioinspired Optimization Methods and Their Applications. BIOMA 2022. Lecture Notes in Computer Science, vol 13627. Springer, Cham. https://doi.org/10.1007/978-3-031-21094-5_11

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

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