Evolutionary optimization of multi-parametric kernel epsilon-SVMr for forecasting problems Articles uri icon

authors

  • GASCON-MORENO, J.
  • ORTIZ GARCIA, EMILIO GEDEON
  • SALCEDO SANZ, SANCHO
  • CARRO-CALVO, L.
  • SAAVEDRA-MORENO, B.
  • PORTILLA FIGUERAS, ANTONIO

publication date

  • February 2013

start page

  • 213

end page

  • 221

issue

  • 2

volume

  • 17

international standard serial number (ISSN)

  • 1432-7643

electronic international standard serial number (EISSN)

  • 1433-7479

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.

keywords

  • support vector regression; machines; algorithms; selection; search