Abstract
Selecting the proper Kernel function in SVMs and the specific parameters for that kernel is an important step in achieving a high performance learning machine. The objective of this research is to optimize SVMs parameters using different kernel functions. We cast this problem as a multi-objective optimization problem, where the classification accuracy, the number of support vectors and the margin define our objective functions. So, we introduce a method based on multi-objective evolutionary algorithm NSGA-II to solve this problem. We also introduce a multi-criteria selection operator for our NSGA-II. The proposed method is applied on some benchmark datasets. The experimental obtained results show the efficiency of the proposed method.
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References
Vapnik, V.: The nature of statistical learning theory. Springer, New York (1995)
Kotsia, I., Pitas, I.: Facial expression recognition in image sequences using geometric deformation features and support vector machines. IEEE Transactions on Image Processing 16(1), 172–187 (2007)
Ma, J., Nguyen, M.N., Rajapakse, J.C.: Gene classification using codon usage and support vector machines. IEEE/ACM Transactions on Computational Biology and Bioinformatics 6(1), 134–143 (2009)
Yu, L., Chen, H., Wang, S., Lai, K.K.: Evolving least squares support vector machines for stock market trend mining. IEEE Transactions on Evolutionary Computation 13(1), 87–102 (2009)
Keerthi, S.S., Lin, C.-J.: Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation 15, 1667–1689 (2003)
Hsu, C.-W., Lin, C.-J.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Networks 13(2), 415–425 (2002)
Staelin, C.: Parameter selection for support vector machines. Hewlett-Packard Co. Tech. Rep. Hpl-2002-354r1 (2003)
Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.: Choosing multiple parameters for support vector machines. Machine Learning 46(1), 131–159 (2002)
Chung, K., Kao, W., Sun, C., Lin, C.: Radius margin bounds for support vector machines with rbf kernel. Neural Comput. 15(11), 2643–2681 (2003)
Frauke, F., Igel, C.: Evolutionary Tuning of Multiple SVM Parameters. In: Proceedings of the 12th European Symposium on Artificial Neural Networks (ESANN 2004). d-side publications, Evere (2004)
Liang, X., Liu, F.: Choosing multiple parameters for SVM based on genetic algorithm. In: 6th International Conference on Signal Processing, August 26-30, vol. 1, pp. 117–119 (2002)
Liu, H.-J., Wang, Y.-N., Lu, X.-F.: A method to choose kernel function and its parameters for support vector machines. In: Proceedings of 2005 International Conference on Machine Learning and Cybernetics, August 18-21, vol. 7, pp. 4277–4280 (2005)
Liu, S., Jia, C.-Y., Ma, H.: A new weighted support vector machine with GA-based parameter selection. In: Proceedings of 2005 International Conference on Machine Learning and Cybernetics, August 18-21, vol. 7, pp. 4351–4355 (2005)
Zhao, M., Fu, C., Ji, L., Tang, K., Zhou, M.: Feature selection and parameter optimization for support vector machines: A new approach based on genetic algorithm with feature chromosomes. Expert Systems with Applications 38(5), 5197–5204 (2011)
Liu, H.-J., Wang, Y.-N., Lu, X.-F.: A method to choose kernel function and its parameters for support vector machines. In: Proceedings of IEEE International Conference on Machine Learning and Cybernetics, vol. 7, pp. 4277–4280 (2005)
Huang, C.L., Wang, C.J.: A GA-based feature selection and parameters optimization for support vector machine. Expert Systems with Application 31(2), 231–240 (2006)
Lin, S.W., Ying, K.C., Chen, S.C., Lee, Z.J.: Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Systems with Applications 35(4), 1817–1824 (2008)
Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering 17(4), 491–502 (2005)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multi-objective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Deb, K., Beyer, H.: Self-adaptive genetic algorithms with simulated binary crossover. Complex Systems 9, 431–454 (1999)
Deb, K., Tiwari, S.: Omni-optimizer: A generic evolutionary algorithm for single and multiobjective optimization. European Journal of Operational Research 185(3), 1062–1087 (2008)
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Bouraoui, A., Ben Ayed, Y., Jamoussi, S. (2014). A Multi-objective Genetic Algorithm for Model Selection for Support Vector Machines. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_64
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DOI: https://doi.org/10.1007/978-3-319-13560-1_64
Publisher Name: Springer, Cham
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