Abstract
We describe a methodology to train Support Vector Machines (SVM) where the regularization parameter (C) is determined automatically via an efficient Genetic Algorithm in order to solve multiple category classification problems. We call the kind of SVMs where C is determined automatically from the application of a GA a “Genetic SVM” or GSVM. In order to test the performance of our GSVM, we solved a representative set of problems by applying one-versus-one majority voting and one-versus-all winner-takes-all strategies. In all of these the algorithm displayed very good performance. The relevance of the problem, the algorithm, the experiments and the results obtained are discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jordaan, E.M., Smits, G.F.: Estimation of the regularization parameter for support vector regression. In: Honolulu, I. (ed.) Proc. of World Conference on Computational Intelligence, Hawaii, pp. 2785–2791 (2002)
Schmitdt, M., Grish, H.: Speaker identification via support vector classifiers. In: Proc. of International Conference on Acoustics, Speech and Signal Processing, pp. 105–108 (1996)
Drucker, H., Wu, D., Vapnik, V.: Support vector machine for spam categorization. Trans. on Neural Networks 10, 1048-1054 (1999)
Vapnik, V., Golowich, S., Smola, A.: Support vector method for function approximation, regression, estimation and signal processing. Adv. Neural Inform. Process. Syst. 9, 281–287 (1996)
Haykin, S.: Neural Networks. A comprehensive foundation, 2nd edn. Prentice Hall, New Jersey (1999)
Mercer, J.: Functions of positive and negative type, and their connection with the theory of integral equations. Transactions of the London Philosophical Society (A) 209, 415–446 (1909)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, UK (2004)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, UK (2000)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Haykin, S.: op. cit., pp. 318–350
Haykin, S.: op. cit., pp. 326–329
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)
Burges, C.J.C.: op. cit., pp. 121–167
Cristianini, N.: op. cit., pp. 93–124
Kuri, A., Gutiérrez, J.: Penalty Functions Methods for Constrained Optimisation with Genetic Algorithms. In: Coello Coello, C.A., de Albornoz, Á., Sucar, L.E., Battistutti, O.C. (eds.) MICAI 2002. LNCS, vol. 2313, pp. 108–117. Springer, Heidelberg (2002)
Kuri, A., Gutiérrez, J.: op. cit., pp.109–111
Fung, G., Mangasarian, O.L.: Multicategory proximal support vector machine classifiers. Data Mining Institute Technical Report, 1–6 (2001)
Allwein, E.L., Shapire, R.E., Singer, Y.: Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research 1, 113–141 (2000)
Bojanov, B., Hakopian, H., Sahakian, B.: Spline Functions and Multivariate Interpolations. Springer, Heidelberg (1993)
Kuri, A., Mejía, I.: Evolutionary Training of SVM for Classification Problems with Self- Adaptive Parameters. In: Gelbukh, A., Monroy, R. (eds.) Advances in Artificial Intelligence Theory, pp. 207–216. IPN (2005)
Kuri Morales, A.: A Methodology for the Statistical Characterization of Genetic Algorithms. In: Proceedings of the Mexican International Congress on Artificial Intelligence, pp. 79–88. Springer, Heidelberg (2002)
Jordaan, E.M., Smits, G.F.: Estimation of the regularization parameter for support vector regression. In: Honolulu, I. (ed.) Proc. of World Conference on Computational Intelligence, Hawaii, pp. 2785–2791 (2002)
Osuna, E., Freund, R., Girosi, F.: Improved training algorithm for support vector machines. In: Proc. IEEE Neural Networks in Signal Processing 1997 (1997)
Mangasarian, O.L., Support Vector Machine Classification via Parameterless Robust Linear Programming, Data Mining Institute Technical Report 03-01, (2003)
Haykin, S., op. cit., pp. 293-294
Coello Coello, C.A., de Albornoz, Á., Sucar, L.E., Battistutti, O.C. (eds.): MICAI 2002. LNCS (LNAI), vol. 2313. Springer, Heidelberg (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kuri-Morales, Á., Mejía-Guevara, I. (2006). Evolutionary Training of SVM for Multiple Category Classification Problems with Self-adaptive Parameters. In: Sichman, J.S., Coelho, H., Rezende, S.O. (eds) Advances in Artificial Intelligence - IBERAMIA-SBIA 2006. IBERAMIA SBIA 2006 2006. Lecture Notes in Computer Science(), vol 4140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11874850_37
Download citation
DOI: https://doi.org/10.1007/11874850_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45462-5
Online ISBN: 978-3-540-45464-9
eBook Packages: Computer ScienceComputer Science (R0)