Cartesian Genetic Programming is robust against redundant attributes in datasets

  • 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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Metadaten
Author:Henning CuiORCiDGND, Jörg HähnerORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1170419
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/117041
ISBN:978-989-758-721-4OPAC
ISSN:2184-3236OPAC
Parent Title (English):Proceedings of the 16th International Joint Conference on Computational Intelligence - ECTA, November 20-22, 2024, in Porto, Portugal
Publisher:SciTePress
Place of publication:Setúbal
Editor:Francesco Marcelloni, Kurosh Madani, Niki van Stein, Joaquim Filipe
Type:Conference Proceeding
Language:English
Year of first Publication:2024
Publishing Institution:Universität Augsburg
Release Date:2024/11/25
GND-Keyword:Cartesian Genetic Programming; Duplicate Attributes; Redundancy; Datasets
Issue:Volume 1
First Page:108
Last Page:119
DOI:https://doi.org/10.5220/0012974600003837
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Organic Computing
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY-NC-ND 4.0: Creative Commons: Namensnennung - Nicht kommerziell - Keine Bearbeitung (mit Print on Demand)

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