Evolutionary optimization of multi-parametric kernel -SVMr for forecasting problems

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 p...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2013-02, Vol.17 (2), p.213-221
Main Authors: Gascón-Moreno, J., Ortiz-García, E. G., Salcedo-Sanz, S., Carro-Calvo, L., Saavedra-Moreno, B., Portilla-Figueras, A.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Classification
Computational Intelligence
Control
Engineering
Evolutionary algorithms
Focus
Forecasting
Genetic algorithms
Mathematical Logic and Foundations
Mechatronics
Optimization
Robotics
Support vector machines
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Summary: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.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-012-0886-5