GA-based selection of components for heterogeneous ensembles of support vector machines

2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings

Volumn 3, Issue , 2003, Pages 2238-2245

  Coelho, André L V ^a   Lima, Clodoaldo A M ^a   Von Zuben, Fernando J ^a  

^a UNIVERSITY OF CAMPINAS   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SCIENCE; EVOLUTIONARY ALGORITHMS;

CHOICE PROBLEMS; COMPONENT SELECTION; HETEROGENEOUS ENSEMBLES; KERNEL FUNCTION; LEARNING MACHINES; OPTIMAL SUBSETS; PERFORMANCE BOTTLENECKS; SVM MODEL;

SUPPORT VECTOR MACHINES;

EID: 84856720362     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2003.1299950     Document Type: Conference Paper

References (19)

1. U. Alon, N. Barkai, D. A. Notterman, K. Gish, S. Ybarra, D. Mack, and A. J. Levine. Broad pat-terns of gene expression revealed by clustering analysis of tumor and normal colon cancer tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci. USA, 96( 12):6745-6750, 1999.
2. T. Back, D. B. Fogel, and Z. Michalewicz, editors. Handbook of Evolutionary Computation. Institute of Physics Publishing and Oxford University Press, 1997.
3. C. L. Blake and C. J. Merz. The UC1 Repository of Machine Learning. University of California at Irvine, http://ics.uci.edu/mlearn/MLRepository.html, 1998.
4. T. Cover. Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Transactions on Electronic Computers, 14:326-334, 1965.
5. N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines. Cambridge Press, 2000.
6. D. Fogel, E. Wasson III, and E. Boughton. Evolving neural networks for detecting breast cancer. Cancer Letters, 96:49-53, 1995.
7. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. Gene selection for cancer classification using support vector machines. Machine Learning, 46(1-3):389-422, 2002.
8. L. K. Hansen and P. Salamon. Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(10):993-1001, 1990.
9. M. Kearns, Y. Mansour, A. Ng, and D. Ron. An experimental and theoretical comparison of model selection methods. Machine Learning, 27(1):7-50, 1997.
10. H.-C. Kim, S. Pang, H.-M. Je, D. Kim, and S.-Y. Bang. Support vector machine ensemble with bagging. In Procs. Of SVM 2002, volume 2388 of LNCS, pages 397-408. Springer, 2002.
11. S.-I. Lee, J.-H. Ahn, and S.-B. Cho. Exploiting diversity of neural ensembles with speciated evolution. In Procs. Of UCNN'01, pages 808-813, Washington, D.C., 2001. IEEE Press.
12. C. A. M. Lima, A. L. V. Coelho, and F. J. Von Zuben. Ensembles of support vector machines for regression problems. In Procs. Of IJCNN'02, pages 2381-2386, Hawaii, 2002. IEEE Press.
13. C. A. M. Lima, A. L. V. Coelho, and F. J. Von Zuben. Model selection based on VC-dimension for heterogeneous ensembles of support vector machines. In A. Lotfi, J. Garibaldi, and R. John, editors, Procs. Of RASC'02, pages 459-464, Nottingham, December 2002. The Nottingham Trent University.
14. M. P. Perrone and L.N. Cooper. When networks disagree: Ensemble method for neural networks. In R. J. Mammone, editor, Neural Networks for Speech and Image processing, pages 126-142. Chapman- Hall, 1993.
15. D. Ruta and B. Gabrys. Application of the evolutionary algorithms for classifier selection in multiple classifier systems with majority voting. In Procs. of MCS 2001, volume 2096 of LNCS, pages 399-408. Springer, 2001.
16. G. Valentini and T. Dietterich. Bias-variance analysis and ensembles of svm. In Procs. of MCS 2002, volume 2364, pages 222-231. Springer, 2002.
17. V. N. Vapnik. Statistical Learning Theory. John Wiley, 1998.
18. Z.-H. Zhou, J.-X. Wu, Y. Jiang, and S.-F. Chen. Genetic algorithm based selective neural network ensemble. In Procs. Of JCAI 2001, volume 2, pages 797-802. Seattle, 2001. AAAI Press.
19. Z.-H. Zhou, J.-X. Wu, W. Tang, and Z.-Q. Chen. Selectively ensembling neural classifiers. In Procs. of IJCNN 2002, 'volume 2, pages 1411-1415, Hawaii, 2002. IEEE Press.