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Authors: Henning Cui and Jörg Hähner

Affiliation: University of Augsburg, 86159 Augsburg, Germany

Keyword(s): Cartesian Genetic Programming, CGP, Noisy Attributes, Duplicate Attributes.

Abstract: Real world datasets might contain duplicate or redundant attributes—or even pure noise—which may not be filtered out by data preprocessing algorithms. This might be problematic, as it decreases the performance of learning algorithms. Cartesian Genetic Programming (CGP) is able to choose its own input attributes by design. Thus, we hypothesize that CGP should be able to ignore redundant or noise attributes. In this work, we empirically show that CGP is indeed able to handle such problematic datasets. For this task, six different datasets are extended with different kinds of redundancies: Duplicated-, duplicated and noised-, and pure noise attributes. Different numbers of unwanted attributes are examined, and we present our results which indicate that CGP is robust against additional redundant or noisy attributes in a dataset. We show that there is no decrease in performance as well as no change in CGP’s convergence behaviour.

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Paper citation in several formats:
Cui, H. and Hähner, J. (2024). Cartesian Genetic Programming Is Robust Against Redundant Attributes in Datasets. In Proceedings of the 16th International Joint Conference on Computational Intelligence - ECTA; ISBN 978-989-758-721-4; ISSN 2184-3236, SciTePress, pages 108-119. DOI: 10.5220/0012974600003837

@conference{ecta24,
author={Henning Cui and Jörg Hähner},
title={Cartesian Genetic Programming Is Robust Against Redundant Attributes in Datasets},
booktitle={Proceedings of the 16th International Joint Conference on Computational Intelligence - ECTA},
year={2024},
pages={108-119},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012974600003837},
isbn={978-989-758-721-4},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computational Intelligence - ECTA
TI - Cartesian Genetic Programming Is Robust Against Redundant Attributes in Datasets
SN - 978-989-758-721-4
IS - 2184-3236
AU - Cui, H.
AU - Hähner, J.
PY - 2024
SP - 108
EP - 119
DO - 10.5220/0012974600003837
PB - SciTePress

- Science and Technology Publications, Lda.
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