An improved PSO for parameter determination and feature selection of SVR and its application in STLE

Journal of Multiple-Valued Logic and Soft Computing

Volumn 16, Issue 6, 2010, Pages 567-584

  Niu, Dong Xiao ^a   Guo, Ying Chun ^a,b  

^a NORTH CHINA ELECTRIC POWER UNIVERSITY   (China)

^b HEBEI UNIVERSITY   (China)

Author keywords

Feature selection; Parameter determination; Particle swarm optimization (PSO); Short term load forecasting (STLF); Simulated annealing (SA); Support vector regression (SVR)

Indexed keywords

FEATURE SELECTION; FORECASTING ACCURACY; GLOBAL SEARCHING; IMPROVED METHODS; IMPROVED PARTICLE SWARM OPTIMIZATION; IMPROVED PSO; INPUT FEATURES; KERNEL PARAMETER; NORTH CHINA; OPERATIONAL DATA; PARAMETER DETERMINATION; POWER GRIDS; SHORT TERM LOAD FORECASTING; SIMULATED ANNEALING ALGORITHMS; SUPPORT VECTOR REGRESSIONS;

ANNEALING; ELECTRIC LOAD FORECASTING; FEATURE EXTRACTION; PARTICLE SWARM OPTIMIZATION (PSO); POWER GENERATION; REGRESSION ANALYSIS;

SIMULATED ANNEALING;

EID: 77953861516     PISSN: 15423980     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (35)

1. V. N. Vapnik. The Nature of Statistical Learning Theory, Springer-Verlag, NY, USA, 1995.
2. G. P. Zhang. Neural networks for classification: A survey. IEEE Trans. Syst. Man Cybern.-Part C: Appl. Rev. 30(4) (2000), 451-462.
3. C.-L. Huang, C.-J. Wang. A GA-based attribute selection and parameter optimization for support vector machine. Expert Syst. Appl. 31(2) (2006), 231-240.
4. C. W. Hsu, C. J. Lin. A simple decomposition method for support vector machine, Mach. Learn. 46(1-3) (2002), 219-314.
5. J. Kennedy, R. C. Eberhart. Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks. Piscataway, NJ, pp. 1942-1948, 1995.
6. J. Kennedy, R. C. Eberhart, and Y. Shi. Swarm Intelligence. Morgan Kaufmann Publishers Inc., San Francisco, CA, 2001.
7. Zhihua Cui, Xingjuan Cai, Jianchao Zeng, and Guoji Sun. Particle Swarm Optimization with FUSS and RWS for High Dimensional Functions, Applied Mathematics and Computation205(1) (2008), 98-108.
8. Ramana Murthy, G, SenthilArumugam, M., and Loo, C. K. Hybrid particle swarm optimizationalgorithm with fine tuning operators. Int. J. Bio-Inspired Computation 1(1/2) (2009),14-31.
9. Cai, X., Cui, Y., and Tan, Y. Predicted modified PSO with time-varying acceleratorcoefficients. Int. J. Bio-Inspired Computation 1(1/2) (2009), 50-60.
10. F. van den Bergh, A. P. Engelbrecht. A study of particle swarm optimization particletrajectories. Inf. Sci. 176(8) (2006), 937-971.
11. A. Chatterjee, P. Siarry. Nonlinear inertia weight variation for dynamic adaptation in particleswarm optimization. Comput. Operations Res. 33(3) (2006), 859-871.
12. D. A.Yi, G. E. Xiu-run. An improved PSO-basedANN with simulated annealing technique.Neurocomputing 63(1) (2005), 527-533.
13. P.-Y. Yin, S.-S. Yu, P.-P. Wang, and Y.-T. Wang. A hybrid particle swarm optimizationalgorithm for optimal task assignment in distributed systems. Comput. Stand. Interfaces28(4) (2006), 441-450.
14. Parsopoulos, K. E., Kariotou, F., Dassios, G., and Vrahatis, M. N. Tackling magnetoencephalography with particle swarm optimization. Int. J. Bio-Inspired Computation 1(1/2)(2009), 32-49.
15. N. Cristianini, J. Shawe-Taylor. An Introduction to Support Vector Machines, CambridgeUniversity Press, Cambridge, UK, 2000.
16. U. KreBel. Pairwise Classification and Support Vector Machines. Advances in KernelMethods-Support Vector Learning. MIT Press, Cambridge, MA, USA, pp. 254-268, 1999.
17. Peressini, A. L., Sullivan, F. E. and Uhl, J. J. The mathematics of nonlinear programming.Springer, New York, 1988.
18. Vladimir Cherkassky, Yunqian Ma. Practical selection of SVM parameters and noiseestimation for SVM regression. Neural Networks 17(1) (2004), 113-126.
19. Y. Shi, R. C. Eberhart. A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, Piscataway, NJ, pp. 69-73, 1998.
20. R. C. Eberhart, Y. Shi. Particle swarm optimization: Developments, application andresources. In: Proceedings of the 2001 Congress on Evolutionary Computation vol.1,pp. 81-86, 2001.
21. J. Kennedy, R. C. Eberhart. A discrete binary version of the particle swarm algorithm.In: Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics.Piscataway, NJ, pp. 4104-4109, 1997.
22. V. Kecman. Learning and Soft Computing, MIT Press, Cambridge, MA, USA, 2001.
23. D. K.Agrafiotis,W. Cedeno. Feature selection for structure-activity correlation using binaryparticle swarms. J. Med. Chem. 45(5) (2002), 1098-1107.
24. C.W. Hsu, C. C. Chang, C. J. Lin. (2003).Apractical guide to support vector classification,available at: http://www.csie.ntu.edu.tw/cjlin/papers/ guide/guide.pdf.
25. H. T. Lin, C. J. Lin. (2003). A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods, Technical Report, Department of Computer Scienceand Information Engineering, National Taiwan University, available at: http://www.csie.ntu.edu.tw/cjlin/papers/tanh.pdf.
26. W. F. Punch, E. D. Goodman, M. Pei, C.-S. Lai, P. Hovland, and R. Enbody. Further researchon feature selection and classification using genetic algorithms, in: Stephanie Forrest (ed.),Fifth International Conference on Genetic Algorithms. San Mateo, CA, Morgan Kaufmann,pp. 557-564, 1993.
27. S. L. Salzberg. On comparing classifiers: pitfalls to avoid and a recommended approach.Data Min. Knowl. Discov. 1 (1997), 317-327.
28. C.-C. Chang, C.-J. Lin. (2001). LIBSVM: A library for support vector machines, Softwareavailable at: http://www.csie.ntu.edu.tw/cjlin/libsvm.
29. P. M. Murphy, D. W. Aha. (2001). UCI Repository of machine learning databases,Department of Information and Computer Science. University of California, Irvine, CA,http://www.ics.uci.edu/mlearn/MLRepository.html .
30. Tay, F. E. H., Cao, L. J. Modified support vector machines in financial time series forecasting.Neurocomputing 48(10) (2002), 847-861.
31. PAI Ping-feng , HONG Wei-chiang. Support vector machines with simulated annealingalgorithms in electricity load forecasting. Energy Conversion and Management 46(17)(2005), 2669-2688.
32. PAI Ping-feng , HONGWei-chiang. Forecasting regional electricity load based on recurrentsupport vector machines with genetic algorithms. Electric Power Systems Research 74(3)(2005), 417-425.
33. CAO Li-juan. Support vector machines experts for time series forecasting. Neurocomputing51(4) (2003), 321-339.
34. HUANGWei, Nakamori Y,WANG Shou-yang. Forecasting stock market movement directionwith support vector machine. Computers and Operations Research 32(10) (2005),2513-2522.
35. Frauke Friedrichs, Christian Igel. Evolutionary tuning of multiple SVM parameters.Neurocomputing 64 (2005), 107-117.