Abstract
The aim of this paper is to define a general frame for building optimal multiple SVM kernels. Our scheme follows 5 steps: formal representation of the multiple kernels, structural representation, choice of genetic algorithm, SVM algorithm, and model evaluation. The computation of the optimal parameter values of SVM kernels is performed using an evolutionary method based on the SVM algorithm for evaluation of the quality of chromosomes. After the multiple kernel is found by the genetic algorithm we apply cross validation method for estimating the performance of our predictive model. We implemented and compared many hybrid methods derived from this scheme. Improved co-mutation operators are used and a comparative study about their effect on the predictive model performances is made. We tested our multiple kernels for classification tasks but they can be also used for other types of tasks.
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
Aizerman, M., Braverman, E., Rozonoer, L.: Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control 25, 821–837 (1964)
Bonabeau, E., Theraulaz, G., Demeubourg, J.I.: Fixed response thresholds and the regulation of division of labor in insect societies. Bull. Math. Biol. 60, 753–807 (1998)
Campbell, C.: An Introduction to Kernel Methods Radial Basis Function Network: Design and Applications, pp. 1–31. Springer, Berlin (2000)
Cicirelo, V.A., Smith, S.F.: Wasp-like Agents for Distributed Factory coordination. Autonomous Agents and Multi-Agent Systems 8(3), 237–267 (2004)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm
Diosan, L., Oltean, M., Rogozan, A., Pecuchet, J.P.: Improving SVM Performance Using a Linear Combination of Kernels. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds.) ICANNGA 2007. LNCS, vol. 4432, pp. 218–227. Springer, Heidelberg (2007)
Diosan, L., Oltean, M., Rogozan, A., Pecuchet, J.P.: Genetically Designed Multiple-Kernels for Improving the SVM Performance, portal VODEL (2008), http://vodel.insa-rouen.fr/publications/rfia
Minh, H.Q., Niyogi, P., Yao, Y.: Mercer’s Theorem, Feature Maps, and Smoothing, http://people.cs.uchicago.edu/~niyogi/papersps/MinNiyYao06.pdf
De Falco, I., Iazzetta, A., Della Cioppa, A., Tarantino, E.: A new mutation operator for evolutionary airfoil design. In: Soft Computing - A Fusion of Foundations, Methodologies and Applications, vol. 3(1), pp. 44–51. Springer, Heidelberg (1999)
Simian, D.: A Model For a Complex Polynomial SVM Kernel. In: Proceedings of the 8th WSEAS Int. Conf. on Simulation, Modelling and Optimization, Santander Spain, within Mathematics and Computers in Science and Engineering, pp. 164–170 (2008)
Simian, D., Stoica, F.: An evolutionary method for constructing complex SVM kernels. In: Proceedings of the 10th International Conference on Mathematics and Computers in Biology and Chemistry, MCBC 2009, Recent Advances in Mathematics and Computers in Biology and Chemistry, Prague, Chech Republic, pp. 172–178. WSEAS Press (2009)
Stoica, F., Simian, D., Simian, C.: A new co-mutation genetic operator. In: Proceeding of the 9th Conference on Evolutionay Computing, Advanced Topics on Evolutionary Computing, Sofia, pp. 76–82 (2008)
Simian, D., Stoica, F.: Evaluation of a hybrid method for constructing multiple SVM kernels. In: Proceedings of the 13th WSEAS International Conference on Computers. Recent Advances in Computer Engineering Series, pp. 619–623. WSEAS Press (2009)
Simian, D., Stoica, F., Simian, C.: Optimization of Complex SVM Kernels Using a Hybrid Algorithm Based on Wasp Behaviour. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds.) LSSC 2009. LNCS, vol. 5910, pp. 361–368. Springer, Heidelberg (2010)
Sonnenburg, S., Rätsch, G., Schäfer, C., Scholkopf, B.: Large scale multiple kernel learning. Journal of Machine Learning Research 7, 1531–1565 (2006)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995), http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Simian, D., Stoica, F. (2012). A General Frame for Building Optimal Multiple SVM Kernels. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2011. Lecture Notes in Computer Science, vol 7116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29843-1_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-29843-1_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29842-4
Online ISBN: 978-3-642-29843-1
eBook Packages: Computer ScienceComputer Science (R0)