Abstract
Support Vector Machines (SVMs) are machine learning models with many diverse applications. The performance of these models depends on a set of assigned hyper-parameters. The task of hyper-parameter tuning has been performed by metaheuristics methods and recent studies have shown that the effectiveness of these methods is statistically equivalent. In this work we compare four bio-inspired metaheuristics (Bat Algorithm, Firefly Algorithm, Particle Swarm Optimization Algorithm and Social Emotional Optimization Algorithm) to test the hypothesis that the efficiency among these differs while the effectiveness remains. Experimental results on several classification problems indicate that there exist bio-inspired algorithms with higher efficiency, in terms of the required number of SVM evaluations to find optimal hyper-parameters. Based on these results the Bat Algorithm is recommended for SVM hyper-parameter tuning.
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Acknowledgements
This work was partially supported by the National Council of Science and Technology (CONACYT) of Mexico [grant numbers: 375524 (Luis C. Padierna), CATEDRAS-2598 (A. Rojas), 416761 (Adán Godínez)].
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Godínez-Bautista, A., Padierna, L.C., Rojas-Domínguez, A., Puga, H., Carpio, M. (2018). Bio-inspired Metaheuristics for Hyper-parameter Tuning of Support Vector Machine Classifiers. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications. Studies in Computational Intelligence, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-319-71008-2_10
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DOI: https://doi.org/10.1007/978-3-319-71008-2_10
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