loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Michael Heider ; Helena Stegherr ; David Pätzel ; Roman Sraj ; Jonathan Wurth ; Benedikt Volger and Jörg Hähner

Affiliation: Universität Augsburg, Am Technologiezentrum 8, Augsburg, Germany

Keyword(s): Rule Set Learning, Rule-based Learning, Learning Classifier Systems, Evolutionary Machine Learning, Interpretable Models, Explainable AI.

Abstract: To fill the increasing demand for explanations of decisions made by automated prediction systems, machine learning (ML) techniques that produce inherently transparent models are directly suited. Learning Classifier Systems (LCSs), a family of rule-based learners, produce transparent models by design. However, the usefulness of such models, both for predictions and analyses, heavily depends on the placement and selection of rules (combined constituting the ML task of model selection). In this paper, we investigate a variety of techniques to efficiently place good rules within the search space based on their local prediction errors as well as their generality. This investigation is done within a specific LCS, named SupRB, where the placement of rules and the selection of good subsets of rules are strictly separated in contrast to other LCSs where these tasks sometimes blend. We compare a Random Search, (1,λ)-ES and three Novelty Search variants. We find that there is a definitive need to guide the search based on some sensible criteria, i.e. error and generality, rather than just placing rules randomly and selecting better performing ones but also find that Novelty Search variants do not beat the easier to understand (1,λ)-ES. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 100.28.0.143

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Heider, M.; Stegherr, H.; Pätzel, D.; Sraj, R.; Wurth, J.; Volger, B. and Hähner, J. (2022). Approaches for Rule Discovery in a Learning Classifier System. In Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - ECTA; ISBN 978-989-758-611-8; ISSN 2184-3236, SciTePress, pages 39-49. DOI: 10.5220/0011542000003332

@conference{ecta22,
author={Michael Heider. and Helena Stegherr. and David Pätzel. and Roman Sraj. and Jonathan Wurth. and Benedikt Volger. and Jörg Hähner.},
title={Approaches for Rule Discovery in a Learning Classifier System},
booktitle={Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - ECTA},
year={2022},
pages={39-49},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011542000003332},
isbn={978-989-758-611-8},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - ECTA
TI - Approaches for Rule Discovery in a Learning Classifier System
SN - 978-989-758-611-8
IS - 2184-3236
AU - Heider, M.
AU - Stegherr, H.
AU - Pätzel, D.
AU - Sraj, R.
AU - Wurth, J.
AU - Volger, B.
AU - Hähner, J.
PY - 2022
SP - 39
EP - 49
DO - 10.5220/0011542000003332
PB - SciTePress