ABSTRACT
A novel Immune Particle Swarm Optimization (IPSO) for parameters optimization of Support Vector Regression (SVR) is proposed in this article. After introduced clonal copy and mutation process of Immune Algorithm (IA), the particle of PSO is considered as antibodies. Therefore, evaluated the fitness of particles by the Leave-One-Out Cross-Validation (LOOCV) standard, the best individual mutated particle for each cloned group will be selected to compose the next generation to get better parameters of SVR. It can construct high accuracy and generalization performance regression model rapidly by optimizing the combination of three SVR parameters at the same time. Under the datasets generated from sinx function with additive noise and spectra dataset, simulation results show that the new method can determine the parameters of SVR quickly and the gotten models have superior learning accuracy and generalization performance.
- Vapnik, V. 1995, The Nature of Statistical Learning Theory, New York: Spring-Verlag Press. Google ScholarDigital Library
- Vapnik, V. 1999, An overview of Statistical Learning Theory. IEEE Trans. On Neural Networks, 10(5):988--999, Google ScholarDigital Library
- Bennett, K. and Campbell, C. 2000, Support vector machine: hype on hallelujah, SIGKDD Exploration, 2(2):1--13 Google ScholarDigital Library
- Smola,A.J. and Scholkopf,B. 2004, A tutorial on support vector regression, Statistics and Computing, 14:199--222 Google ScholarDigital Library
- Kennedy J. and Eberhart, R. C. 1995, "Particle Swarm Optimization," in Proc. IEEE Conf. Neural Networks IV, Vol. 4, pp. 1942--1948.Google Scholar
- Shi, Y., and Eberhart, R. C., 1998, "A Modified Particle Swarm Optimizer," Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 69--73.Google Scholar
- R.C. Eberhart, and Shi, Y. 2001, Particle Swarm Optimization: Developments, Applications and Resources, in: Proceedings of International Conference on Evolutionary Computation, pp. 81--86.Google Scholar
- Hunt, J. E. and Cooke, D. E. 1996, "Learning using an artificial immune system", Journal of Network and Computer Applications, vol. 19, pp. 189--212. Google ScholarDigital Library
- Cherkassky,V. and Ma,Y. 2004, Practical selection of SVM parameters and noise estimation for SVM regression {J } . Neural Networks, 17 (1) :113--126. Google ScholarDigital Library
- Xin Wang, Chunhua Yang, Bin Qin, Weihua Gui. 2005, Parameter selection of support vector regression based on hybrid optimization algorithm and its application. Journal of Control Theory and Applications, 2005, 4 : 371--376Google ScholarCross Ref
- Kalivas, John H., 1997, "Two Data Sets of Near Infrared Spectra," Chemometrics and Intelligent Laboratory Systems, v.37 (1997) pp.255--259Google ScholarCross Ref
- Haenlein, Michael and Andreas M. Kaplan , 2004, "A Beginner's Guide to Partial Least Squares Analysis, Understanding Statistics, 3(4), 283--297".Google ScholarCross Ref
Index Terms
- Parameters optimization of support vector regression based on immune particle swarm optimization algorithm
Recommendations
Immune Particle Swarm Optimization for Support Vector Regression on Forest Fire Prediction
ISNN 2009: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part IIAn Immune Particle Swarm Optimization (IPSO) for parameters optimization of Support Vector Regression (SVR) is proposed in this article. After introduced clonal copy and mutation process of Immune Algorithm (IA), the particle of PSO is considered as ...
Parameter Selection of Support Vector Regression Based on Particle Swarm Optimization
GRC '10: Proceedings of the 2010 IEEE International Conference on Granular ComputingParameters selection of support vector machine is the key issue that impacts its accurate performance. A method for support vector regression machine with basic particle swarm optimization (BPSO) algorithm is proposed in this paper. Furthermore, in ...
An improved cooperative quantum-behaved particle swarm optimization
Particle swarm optimization (PSO) is a population-based stochastic optimization. Its parameters are easy to control, and it operates easily. But, the particle swarm optimization is a local convergence algorithm. Quantum-behaved particle swarm ...
Comments