Genetic Folding: Analyzing the Mercer-s Kernels Effect in Support Vector Machine using Genetic Folding
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.
[1] Cristianini, N. and Shawe-Taylor, J., 'An Introduction to Support
Vector Machines: and Other Kernel-Based Learning Methods', 1st
edn. Cambridge University Press, (2000).
[2] 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).
[3] Dioşan, 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).
[4] Diosan, L., Rogozan, A. and Pecuchet, J-P. , 'Evolving Kernel
Functions for SVMs by Genetic Programming', Machine Learning
and Applications, ICMLA, 19-24 (2007).
[5] 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).
[6] 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).
[7] Koza, J. R., 'Genetic Programming: on the Programming of
Computers by Means of Natural Selection', 74-147, Cambridge,
MA: The MIT Press, (1992).
[8] 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).
[9] Rojas, S.A. and Fernandez-Reyes, D., 'Adapting Multiple Kernel
Parameters for Support Vector Machines using Genetic Algorithms',
IEEE, vol. 1. 626-631 (2005).
[10] Silva S., 'GPLAB: A Genetic Programming Toolbox for MATLAB',
(2007).
[11] Sivanandam, S. and Deepa, S., 'Introduction to Genetic Algorithm',
Springer, 15-130 (2008).
[12] Staelin C., 'Parameter Selection for Support Vector Machines', HP
Laboratories, (2003).
[13] Sullivan, K. and Luke, S., 'Evolving Kernels for Support Vector
Machine Classification', Genetic And Evolutionary Computation
Conference, 1702 - 1707 (2007).
[14] Mezher, M., Abbod, M. ÔÇÿEvolving Self-Adaptive Genetic Algorithm
using Nonlinear Support Vector for Classification Problems-. The
International Journal Annals Computer Science Series. (2010).
[15] Mezher, M., Abbod, M. ÔÇÿPalindrome Genetic Folding for Support
Vector Regression Problems-. International Journal of Computer
Systems Science and Engineering. Submitted on December (2010).
[16] Mohd Mezher, Maysam Abbod. ÔÇÿGenetic Folding: A New
Algorithm for Solving Multiclass SVM Problems-. Applied Soft
Computing, Elsiver Journal. Submitted on September (2010).
[17] 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).
[18] Vapnik V.N., ÔÇÿStatistical Learning Theory-. 1998, John Wiley and
Sons: USA.
[19] Chang C., Lin J., ÔÇÿLIBSVM: A Library for Support Vector
Machines-. in 8.1. (2001).
[20] 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).
[21] 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)
[22] John Shawe-Taylor, Cristianini N., ÔÇÿKernel Methods for Pattern
Analysis-. (2004), Cambridge University Press: UK.
[1] Cristianini, N. and Shawe-Taylor, J., 'An Introduction to Support
Vector Machines: and Other Kernel-Based Learning Methods', 1st
edn. Cambridge University Press, (2000).
[2] 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).
[3] Dioşan, 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).
[4] Diosan, L., Rogozan, A. and Pecuchet, J-P. , 'Evolving Kernel
Functions for SVMs by Genetic Programming', Machine Learning
and Applications, ICMLA, 19-24 (2007).
[5] 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).
[6] 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).
[7] Koza, J. R., 'Genetic Programming: on the Programming of
Computers by Means of Natural Selection', 74-147, Cambridge,
MA: The MIT Press, (1992).
[8] 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).
[9] Rojas, S.A. and Fernandez-Reyes, D., 'Adapting Multiple Kernel
Parameters for Support Vector Machines using Genetic Algorithms',
IEEE, vol. 1. 626-631 (2005).
[10] Silva S., 'GPLAB: A Genetic Programming Toolbox for MATLAB',
(2007).
[11] Sivanandam, S. and Deepa, S., 'Introduction to Genetic Algorithm',
Springer, 15-130 (2008).
[12] Staelin C., 'Parameter Selection for Support Vector Machines', HP
Laboratories, (2003).
[13] Sullivan, K. and Luke, S., 'Evolving Kernels for Support Vector
Machine Classification', Genetic And Evolutionary Computation
Conference, 1702 - 1707 (2007).
[14] Mezher, M., Abbod, M. ÔÇÿEvolving Self-Adaptive Genetic Algorithm
using Nonlinear Support Vector for Classification Problems-. The
International Journal Annals Computer Science Series. (2010).
[15] Mezher, M., Abbod, M. ÔÇÿPalindrome Genetic Folding for Support
Vector Regression Problems-. International Journal of Computer
Systems Science and Engineering. Submitted on December (2010).
[16] Mohd Mezher, Maysam Abbod. ÔÇÿGenetic Folding: A New
Algorithm for Solving Multiclass SVM Problems-. Applied Soft
Computing, Elsiver Journal. Submitted on September (2010).
[17] 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).
[18] Vapnik V.N., ÔÇÿStatistical Learning Theory-. 1998, John Wiley and
Sons: USA.
[19] Chang C., Lin J., ÔÇÿLIBSVM: A Library for Support Vector
Machines-. in 8.1. (2001).
[20] 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).
[21] 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)
[22] John Shawe-Taylor, Cristianini N., ÔÇÿKernel Methods for Pattern
Analysis-. (2004), Cambridge University Press: UK.
@article{"International Journal of Information, Control and Computer Sciences:54900", author = "Mohd A. Mezher and Maysam F. Abbod", title = "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.", keywords = "Genetic Folding, GF, Evolutionary Algorithms,Support Vector Machine, Genetic Algorithm, Genetic Programming,Multi-Classification, Mercer's Rules", volume = "5", number = "3", pages = "260-6", }
