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RESEARCH ARTICLE

A snake optimizer-based kernel extreme learning machine for classification

Haolong Yang1 ,  Qi Diao1,2* ,  WengHowe Chan2*
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1 Faculty of Artificial Intelligence, Zhejiang Dongfang Polytechnic, Wenzhou, China
2 Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia
IJOCTA, 026150058 https://doi.org/10.36922/IJOCTA026150058
Received: 7 April 2026 | Revised: 12 May 2026 | Accepted: 19 May 2026 | Published online: 5 June 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Kernel Extreme Learning Machine (KELM) is well-suited for nonlinear classification, but its performance is highly sensitive to the regularization parameter C and the RBF kernel width σ. To reduce manual tuning, we proposes a Snake Optimizer-based KELM model, termed SKELM, which jointly optimizes C and σ using validation accuracy as the fitness function. Experiments on ten UCI benchmark datasets indicate that compared with KELM, LSOKELM, and SSAKELM, SKELM achieves the unique best accuracy on five datasets and ties for the best accuracy on the remaining three datasets. The proposed method also shows fewer average iterations and a lower standard deviation under the current experimental setting. These results suggest that SKELM can serve as a effective parameter-search framework for KELM, although further validation with additional baselines, statistical tests, convergence curves, runtime comparisons, and more evaluation metrics remains necessary.

Keywords
Extreme learning machine
Snake optimizer
Swarm intelligence
Parameter optimization
Classification
Funding
Conflict of interest
The authors declare they have no competing interests.
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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing
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