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Evolutionary optimization of multi-parametric kernel ε-SVMr for forecasting problems
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DOI：10.1007/s00500-012-0886-5.png)
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Abstract
En 中文
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.
Keywords:
SUPPORT VECTOR MACHINES
REGRESSION
SEARCH
Journal
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2.5
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Citations:
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