Commenced in January 2007
Paper Count: 31100
Genetic Folding: Analyzing the Mercer-s Kernels Effect in Support Vector Machine using Genetic Folding
Abstract:Genetic Folding (GF) a new class of EA named as is introduced for the first time. It is based on chromosomes composed of floating genes structurally organized in a parent form and separated by dots. Although, the genotype/phenotype system of GF generates a kernel expression, which is the objective function of superior classifier. In this work the question of the satisfying mapping-s rules in evolving populations is addressed by analyzing populations undergoing either Mercer-s or none Mercer-s rule. The results presented here show that populations undergoing Mercer-s rules improve practically models selection of Support Vector Machine (SVM). The experiment is trained multi-classification problem and tested on nonlinear Ionosphere dataset. The target of this paper is to answer the question of evolving Mercer-s rule in SVM addressed using either genetic folding satisfied kernel-s rules or not applied to complicated domains and problems.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1061735Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1319
 Cristianini, N. and Shawe-Taylor, J., 'An Introduction to Support Vector Machines: and Other Kernel-Based Learning Methods', 1st edn. Cambridge University Press, (2000).
 Chen, P-W., Wang, J-Y. and Lee, H-M., 'Model Selection of SVMs Using GA Approach', IEEE International Joint Conference, vol. 3, 2035- 2040 (2004).
 Dio┼ƒan, L., Rogozan, A. and Pecuchet, J-P.,- Optimising Multiple Kernels for SVM by Genetic Programming-, Evolutionary Computation in Combinatorial Optimization, vol. 4972, 230-241 (2008).
 Diosan, L., Rogozan, A. and Pecuchet, J-P. , 'Evolving Kernel Functions for SVMs by Genetic Programming', Machine Learning and Applications, ICMLA, 19-24 (2007).
 Gagné, C., Schoenauer, M., Sebag, M. and Tomassini, M., 'Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection', LNCS, no. 4193, 1008-1017 (2006).
 Howley, T. and Madden M., 'The Genetic Kernel Support Vector Machine: Description and Evaluation', Artificial Intelligence Review, vol. 24, no. 3-4, 379-395 (2005).
 Koza, J. R., 'Genetic Programming: on the Programming of Computers by Means of Natural Selection', 74-147, Cambridge, MA: The MIT Press, (1992).
 Lessmann, S., Stahlbock, R. and Crone, S. F., 'Genetic Algorithms for Support Vector Machine Model Selection', Proc. of the Intern. Joint Conf. on Neural Networks (IJCNN'06), Vancouver, Canada, (2006).
 Rojas, S.A. and Fernandez-Reyes, D., 'Adapting Multiple Kernel Parameters for Support Vector Machines using Genetic Algorithms', IEEE, vol. 1. 626-631 (2005).
 Silva S., 'GPLAB: A Genetic Programming Toolbox for MATLAB', (2007).
 Sivanandam, S. and Deepa, S., 'Introduction to Genetic Algorithm', Springer, 15-130 (2008).
 Staelin C., 'Parameter Selection for Support Vector Machines', HP Laboratories, (2003).
 Sullivan, K. and Luke, S., 'Evolving Kernels for Support Vector Machine Classification', Genetic And Evolutionary Computation Conference, 1702 - 1707 (2007).
 Mezher, M., Abbod, M. ÔÇÿEvolving Self-Adaptive Genetic Algorithm using Nonlinear Support Vector for Classification Problems-. The International Journal Annals Computer Science Series. (2010).
 Mezher, M., Abbod, M. ÔÇÿPalindrome Genetic Folding for Support Vector Regression Problems-. International Journal of Computer Systems Science and Engineering. Submitted on December (2010).
 Mohd Mezher, Maysam Abbod. ÔÇÿGenetic Folding: A New Algorithm for Solving Multiclass SVM Problems-. Applied Soft Computing, Elsiver Journal. Submitted on September (2010).
 Mohd Mezher, Maysam Abbod. ÔÇÿGenetic Folding: A New Class of Evolutionary Algorithm for SVM-. Society's Specialist Group on Artificial Intelligence (SGAI) International Conference on Artificial Intelligence. Cambridge, UK. August (2010).
 Vapnik V.N., ÔÇÿStatistical Learning Theory-. 1998, John Wiley and Sons: USA.
 Chang C., Lin J., ÔÇÿLIBSVM: A Library for Support Vector Machines-. in 8.1. (2001).
 Lessmann S., Stahlbock R. and Crone F. ÔÇÿGenetic Algorithms for Support Vector Machine Model Selection-. in International Joint Conference on Neural Network. (2006). Vancouver, Canada,: Proc. of the Intern. Joint Conf. on Neural Networks (IJCNN'06).
 Kim H., Holand P., Park H. and Christianini N., ÔÇÿDimension Reduction in Text Classification with Support Vector Machines-. Journal of Machine Learning Research,. 6: 37-53. (2005)
 John Shawe-Taylor, Cristianini N., ÔÇÿKernel Methods for Pattern Analysis-. (2004), Cambridge University Press: UK.