Kernel extreme learning machine optimized by arithmetic optimization algorithm

Changlin Li; Jinpeng Wang; Guanhua Peng; Jinyi Hu

doi:10.1117/12.3035266

21 July 2024 Kernel extreme learning machine optimized by arithmetic optimization algorithm

Changlin Li, Jinpeng Wang, Guanhua Peng, Jinyi Hu

Proceedings Volume 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024); 132192W (2024) https://doi.org/10.1117/12.3035266
Event: 4th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2024), 2024, Kaifeng, China

Abstract

The kernel function is introduced by the Kernel Extreme Learning Machine on the basis of the original Extreme Learning Machine, which effectively improves the capability of prediction of the Extreme Learning Machine, but the hyperparameters of it still affect the model performance to a large extent. As an effective metaheuristic algorithm, the Arithmetic Optimization Algorithm is frequently utilized to address optimization problems. This work introduces the Kernel Extreme Learning Machine optimized by the Arithmetic Optimization Algorithm (AOA-KELM) in an effort to significantly improve the efficiency of the Kernel Extreme Learning Machine. Regularization coefficients and Kernel parameters are the hyperparameters under optimization. On the UCI dataset, the performance of the proposed algorithm is verified through comparison with the Extreme Learning Machine–Grey Wolf Optimizer, Deep Extreme Learning Machine, Backpropagation Algorithm, and Support Vector Machine. The results obtained from the numerical analysis validate the superiority of the proposed method, since the proposed algorithm significantly outperforms its competitors.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Changlin Li, Jinpeng Wang, Guanhua Peng, and Jinyi Hu "Kernel extreme learning machine optimized by arithmetic optimization algorithm", Proc. SPIE 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), 132192W (21 July 2024); https://doi.org/10.1117/12.3035266

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KEYWORDS

Mathematical optimization

Extreme learning machines

Matrices

Education and training

Machine learning

Algorithm development

Artificial neural networks

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