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01.02.2013 | Focus

Evolutionary optimization of multi-parametric kernel \(\epsilon\)-SVMr for forecasting problems

verfasst von: J. Gascón-Moreno, E. G. Ortiz-García, S. Salcedo-Sanz, L. Carro-Calvo, B. Saavedra-Moreno, A. Portilla-Figueras

Erschienen in: Soft Computing | Ausgabe 2/2013

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Abstract

In this paper, we propose a novel multi-parametric kernel Support Vector Regression algorithm (SVMr) optimized with an evolutionary technique, specially well suited for forecasting problems. The multi-parametric SVMr model and the evolutionary algorithm proposed are both described in detail in the paper. In addition, several new bounds for the multi-parametric kernel considered are obtained, in such a way that the SVMr hyper-parameters’ search space is reduced. We present experimental evidences of the good performance of the evolutionary algorithm for optimizing the multi-parametric kernel, when compared to a standard SVMr with a Grid Search approach. Specifically, results in different real regression problems from public repositories are obtained, and also a real application focused on the short-term temperature prediction at Barcelona’s airport. The results obtained have shown the good performance of the multi-parametric kernel approach both in accuracy and computation time.

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Titel: Evolutionary optimization of multi-parametric kernel -SVMr for forecasting problems
verfasst von: J. Gascón-Moreno
E. G. Ortiz-García
S. Salcedo-Sanz
L. Carro-Calvo
B. Saavedra-Moreno
A. Portilla-Figueras
Publikationsdatum: 01.02.2013
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 2/2013
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-012-0886-5

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