Feature selection using PSO-SVM

Lecture Notes in Engineering and Computer Science

Volumn , Issue , 2006, Pages 138-143

  Tu, Chung Jui ^a   Chuang, Li Yeh ^b   Chang, Jun Yang ^a   Yang, Cheng Hong ^a  

^a NATIONAL KAOHSIUNG UNIVERSITY OF APPLIED SCIENCES   (Taiwan)

^b I SHOU UNIVERSITY   (Taiwan)

Author keywords

Feature Selection; Machine Learning; Particle Swarm Optimization; Support Vector Machines

Indexed keywords

CLASSIFICATION ACCURACY; DATA MINING APPLICATIONS; FEATURE SELECTION METHODS; FITNESS FUNCTIONS; PREDICTIVE ACCURACY; PROCESSING RATES; SAMPLE CLASSIFICATION; SUPPORT VECTOR MACHINE (SVMS);

COMPUTER SCIENCE; DATA PROCESSING; FEATURE EXTRACTION; LEARNING SYSTEMS; PARTICLE SWARM OPTIMIZATION (PSO);

SUPPORT VECTOR MACHINES;

EID: 84888264031     PISSN: 20780958     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. Raymer, M. L., Punch, W. F., Goodman, E. D., Kuhn, L. A., and Jain, A. K "Dimensionality Reduction Using Genetic Algorithms," IEEE Trans. Evolutionary Computation, vol. 4, no. 2, pp. 164-171, July 2000
2. Narendra, P. M. and Fukunage, K., "A Branch and Bound Algorithm for Feature Subset Selection," IEEE Trans. Computers, vol. 6, no. 9, pp. 917-922, Sept. 1977
3. Pudil, P., Novovicova, J., and Kittler, J., "Floating Search Methods in Feature Selection," Pattern Recognition Letters, vol. 15, pp. 1119-1125, 1994
4. Roberto B., "Using mutual information for selecting features in supervised neural net learning," IEEE Transactions on Neural Networks, 5(4):537-550, 1994.
5. Zhang, H. and Sun, G. Feature selection using tabu search method. Pattern Recognition, 35: 701-711, 2002
6. Furey, T. S., Cristianini, N., Duffy, N., Bednarski, D. W., Schummer, M. and Haussler, D. (2000) Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 16, 906-914
7. Guyon, I., Weston, J., Barnhill, S. and Vapnik, V. (2002) Gene selection for cancer classification using support vector machines. Machine Learning, 46, 389-422
8. Lee, Y. and Lee, C.-K. (2003) Classification of multiple cancer types by multicategory support vector machines using gene expression data. Bioinformatics, 18, 1132-1139
9. Venter, G. and Sobieszczanski-Sobieski, J. Particle swarm optimization. Proceedings of the 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Denver, CO. 2002
10. Stone, M., "Cross-Validation choice and assessment of statistical predictions" Journal of the Royal Statistical Society B, 36 1974, pp. 111-147
11. Kennedy, J. ; Eberhart, R. C., "A discrete binary version of the particle swarm algorithm", Systems, Man, and Cybernetics, 1997. 'Computational Cybernetics and Simulation'., 1997 IEEE International Conference on Volume 5, 12-15 Oct. 1997 Page(s):4104-4108 vol. 5
12. Kennedy, J. and Eberhart, R. C., Particle swarm optimization, IN proceedings of the 1995 IEEE International Conference on Neural Networkds, volume 4, pages 1942-1948, Perth, Australia, 1995
13. FrieB, T., N. Cristianini, and C. Campbell (1998), "The Kernel-Adatron: a Fast and Simple Learning Procedure for Support Vector Machines," Proceedings of the Fifteenth International Conference on Machine Learning, pp. 188-196, 1998
14. Drucker, H., Burges, C, Kaufman, L, Smola, A. and Vapnik, V. (1997): Support Vector Regression Machines, In: Mozer, M., Jordan, M. and Petsche, T. (ed. ), Neural Information Processing Systems, Vol. 9. MIT Press, Cambridge, MA, 155-161
15. Vapnik, V. N., The Nature of Statistical Learning Theory, Springer Verlag, 1995
16. Anlauf, J. K., and Biehl, M. (1989), The Adatron-an adaptive perceptron algorithm, Europhysics Letters, 10, 687-692
17. Opper, M. (1988). Learning Time of Neural Networks: Exact Solution for a Perceptron Algorithm. Physical Review A38:3824
18. Boser, B., Guyon, I., Vapnik, V. (1992), A training algorithm for optimal margin classifiers, Fifth Annual Workshop on Computational Learning Theory, ACM Press
19. Cortes, C, and Vapnik, V. (1995), Support Vector networks, Machine Learning 20:273-297
20. Colin, C. and Nello, C, "Simple Learning. Algorithms for Training Support Vector Machines," 1998
21. Scholkopf, B. and Smola, A. (2002) Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. MIT Press, Cambridge, MA
22. Murphy, P. M., and Aha, D. W. UCI Repository of Machine Learning Databases, technical report, Department of Information and Computer Science, University of California, Irvine, Calif, 1994. Available: http://www. ics. uci. edu/~mlearn/MLRepositorv. html
23. Oh, I.-S., Lee, J.-S., and Moon, B.-R. Hybrid Genetic Algorithms for Feature Selection. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. ll, Nov. 2004.