Abstract
An appropriate parameters selection can significantly affect the accuracy of support vector regression (SVR) model. In this paper, a new evolutionary approach based on Differential Evolution (DE-SVR) is developed to train the SVR model. The approach evolves automatically the optimal model parameters by the differential mutation operations. Experimental results on several real-world datasets demonstrate that, comparing with the GA-based SVR and the Grid search methods, the DE-SVR can search the optimal parameters much more rapidly with less training time to build the SVR model, and has the comparable prediction accuracy as Grid search, even better than GA-based SVR. Therefore, the new evolutionary DE-SVM approach is an efficient method for automatic parameter determination of SVR problem.
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References
Vapnik, V.N.: The nature of statistical learning theory. Springer, NewYork (1995)
Smola, A.J., et al.: A tutorial on support vector regression. Neuro COLT Technical Report Series, NC-TR-1998-030, Royal Holloway College, University of London, UK, vol. 10, pp. P 1–49 (1998)
Duan, K., Keerthi, S., Poo, A.: Evaluation of simple performance measures for tuning SVM hyperparameters (Technical report). Singapore: National Universityof Singapore, Department of Mechanical Engineering (2001)
Peng, K.L., Wu, C.H., Goo, Y.J.: The development of a new statistical technique for relating financial information to stock market returns. International Journal of Management 21(4), 492–505 (2004)
Storn, R., Price, K.: Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)
Lampinen, J.: A constraint handling approach for the differential evolution algorithm. In: Proc. 2002 IEEE Congress on Evolutionary Computation, Honolulu, Hawaii, May 2002, pp. 1468–1473 (2002)
Storn, R.: System design by constraint adaptation and differential evolution. IEEE Trans. Evol. Comput. 3(1), 22–34 (1999)
Michalewicz, Z., Schoenauer, M.: Evolutionary algorithms for constrained parameter optimization problems. Evol. Comput. 4(1), 1–32 (1996)
Cherkassky, V., Mulier, F.: Learning from data: Concepts, theory, and methods. Wiley, New York (1998)
Scholkopf, B., Burges, J., Smola, A.: Advances in kernel methods: Support vector machine. MIT Press, Cambridge (1999)
Cristianini, N., Shawe-Taylor, J., Campell, C.: Dynamically adapting kernels in support vector machines. Advances in Neural Information Processing Systems 11, 204–210 (1998)
Deb, K., Goyal, M.: A combined genetic adaptive search (geneAS) for engineering design. Computer Science and Informatics 26(4), 30–45 (1996)
Murphy, P.M, Aha, D.W: UCI repository of machine learning datasets (1992), http://www.1ics.uci.edu/~mlearn/MLRepository
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2003), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Bessaou, M., Siarry, P.: A genetic algorithm with real-value coding to optimize multimodal continuous functions. Struct. Multidisc. Optim. 23, 63–74 (2001)
Runarsson, T.P., Sigurdsson, S.: Model selection for support vector machines using an asynchronous parallel evolution strategy. In: IEEE International Conference on Neural Networks and Signal Processing, pp. 495–498 (2003)
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Li, J., Cai, Z. (2008). A Novel Automatic Parameters Optimization Approach Based on Differential Evolution for Support Vector Regression. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_56
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DOI: https://doi.org/10.1007/978-3-540-92137-0_56
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