Abstract
In this paper we propose a novel multi-parametric kernel Support Vector Regression algorithm optimized with a genetic algorithm. The multi-parametric model and the genetic algorithm proposed are both described with detail in the paper. We also present experimental evidences of the good performance of the genetic algorithm, when compared to a standard Grid Search approach. Specifically, results in different real regression problems from public repositories have shown the good performance of the multi-parametric kernel approach both in accuracy and computation time.
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References
Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Statistics and Computing (1998)
He, W., Wang, Z., Jiang, H.: Model optimizing and feature selecting for support vector regression in time series forecasting. Neurocomputing 72(1-3), 600–611 (2008)
Wu, C.L., Chau, K.W., Li, Y.S.: River stage prediction based on a distributed support vector regression. J. of Hydrology 358(1-2), 96–111 (2008)
Ortiz-Garcia, E.G., Salcedo-Sanz, S., Pérez-Bellido, A.M., Portilla-Figueras, J.A.: Improving the training time of support vector regression algorithms through novel hyper-parameters search space reductions. Neurocomputing 72(1-3), 3683–3691 (2009)
Akay, M.F.: Support vector machines combined with feature selection for breast cancer diagnosis. Expert Systems with Applications 36(2), 3240–3247 (2009)
Eiben, A.E., Smith, J.E.: Introduction to evolutionary computing. Springer, Heidelberg (2003)
Wu, G.H., Tzeng, G.H., Lin, R.H.: A Novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression. Expert Systems with Applications 36(3), 4725–4735 (2009)
Hou, S., Li, Y.: Short-term fault prediction based on support vector machines with parameter optimization by evolution strategy. Expert Systems with Applications 36(10), 12383–12391 (2009)
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
StatLib DataSets Archive, http://lib.stat.cmu.edu/datasets
Chang, C.C., Lin, C.-J.: LIBSVM: a library for support vector machines. Software, http://www.csie.ntu.edu.tw/~cjlin/libsvm
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Gascón-Moreno, J., Ortiz-García, E.G., Salcedo-Sanz, S., Paniagua-Tineo, A., Saavedra-Moreno, B., Portilla-Figueras, J.A. (2011). Multi-parametric Gaussian Kernel Function Optimization for ε-SVMr Using a Genetic Algorithm. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21498-1_15
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DOI: https://doi.org/10.1007/978-3-642-21498-1_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21497-4
Online ISBN: 978-3-642-21498-1
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