Article

Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system

Author:

Martin V. ButzAuthors Info & Claims

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

Pages 1835 - 1842

https://doi.org/10.1145/1068009.1068320

Published: 25 June 2005 Publication History

Abstract

Many learning classifier system (LCS) implementations are restricted to the binary problem realm. Recently, the XCS classifier system was enhanced to be able to handle real-valued inputs among others. In the real-valued enhancement, XCSF applies as a function approximation system that partitions the input space in hyperrectangular subspaces specified in the classifiers. This paper changes the classifier conditions to hyperspheres and hyperellipsoids and investigates the consequent performance impact. It is shown that the modifications yield improved performance in continuous functions. Even in discontinuous functions with parallel boundaries, XCS's performance does not degrade. Thus, for the real-valued problem domain, ellipsoidal condition structures can improve XCS's performance. From a more general perspective, this paper shows that XCS is readily applicable in diverse problem domains. To apply the system even more successfully, suitable kernel-based bases need to be found and used as classifier conditions. XCS distributes the available structures over the problem space evolving more specialized structures in more complex problem subspaces.

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Cited By

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Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system

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cover image ACM Conferences

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

June 2005

2272 pages

ISBN:1595930108

DOI:10.1145/1068009

Editor:
Hans-Georg Beyer
Vorarlberg University of Applied Sciences, Austria
,
General Chair:
Una-May O'Reilly
CSAIL, MIT

Copyright © 2005 ACM.

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Published: 25 June 2005

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June 25 - 29, 2005

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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https://dl.acm.org/doi/10.1016/j.asoc.2023.110706
Chen QXue BBrowne WZhang M(2023)Evolutionary Regression and ModellingHandbook of Evolutionary Machine Learning10.1007/978-981-99-3814-8_5(121-149)Online publication date: 2-Nov-2023
https://doi.org/10.1007/978-981-99-3814-8_5
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https://dl.acm.org/doi/10.1145/3512290.3528874
Shiraishi HHavamizu YSato HTakadama K(2022)Beta Distribution based XCS Classifier System2022 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC55065.2022.9870314(1-8)Online publication date: 18-Jul-2022
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Heider MStegherr HWurth JSraj RHähner J(2022)Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier SystemBioinspired Optimization Methods and Their Applications10.1007/978-3-031-21094-5_11(142-156)Online publication date: 10-Nov-2022
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Tadokoro MSato HTakadama K(2021)XCS with Weight-based Matching in VAE Latent Space and Additional Learning of High-Dimensional Data2021 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC45853.2021.9504909(304-310)Online publication date: 28-Jun-2021
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Fredivianus NGeihs K(2017)Classifier systems with native fuzzy logic control operationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3082487(1341-1348)Online publication date: 15-Jul-2017
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