Particle swarm optimization based multi-prototype ensembles

Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009

Volumn , Issue , 2009, Pages 57-63

  Mohemmed, Ammar ^a   Johnston, Mark ^a   Zhang, Mengjie ^a  

^a VICTORIA UNIVERSITY OF WELLINGTON   (New Zealand)

Author keywords

Classification; Ensemble; Particle swarm optimization

Indexed keywords

CLASSIFICATION; DIVERSE ENSEMBLES; ENSEMBLE CLASSIFIERS; ERROR RATE; FITNESS FUNCTIONS; MAJORITY VOTING MECHANISM; NEAREST PROTOTYPE; NEW MEMBERS; PENALTY TERM; SIMULATION EXPERIMENTS;

CLASSIFIERS; FUNCTION EVALUATION; LEARNING SYSTEMS;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 72749106588     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1569901.1569910     Document Type: Conference Paper

References (36)

1. UCI repository of machine learning databases. http://archive.ics.uci.edu/ ml/datasets.html.
2. R. Banfield, L. Hall, K. Bowyer, and W. Kegelmeyer. A comparison of decision tree ensemble creation techniques. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(1):173-180, 2007.
3. J. Bezdek, T. Reichherzer, G. Lim, and Y. Attikiouzel. Multiple-prototype classifier design. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 28(1):67-79, 1998.
4. L. Breiman. Bagging predictors. Machine Learning, 24(2):123-140, 1996.
5. G. Brown and J. Wyatt. The use of the ambiguity decomposition in neural network ensemble learning methods. In T. Fawcett, et al., editors, ICML'03, pages 67-74, 2003.
6. G. Brown, J. Wyatt, R. Harris, and X. Yao. Diversity creation methods: a survey and categorisation. Information Fusion, 6(1):5 - 20, 2005.
7. D. W. Bunn. Statistical efficiency in the linear combination of forecasts. International Journal of Forecasting, 1(2):151-163, 1985.
8. D. W. Bunn. Forecasting with more than one model. Journal of Forecasting, 8(3):161-166, 1989.
9. A. Cervantes, I. Galvan, and P. Isasi. Building nearest prototype classifiers using a Michigan approach PSO. IEEE Swarm Intelligence Symposium (SIS 2007), pages 135-140, 2007.
10. I. De Falco, A. Della Cioppa, and E. Tarantino. Facing classification problems with particle swarm optimization. Appl. Soft Comput., 7(3):652-658, 2007.
11. F. Fernández and P. Isasi. Evolutionary design of nearest prototype classifiers. Journal of Heuristics, 10(4):431-454, 2004.
12. Y. Freund and R. E. Schapire. Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference on Machine Learning, pages 148-156. Morgan Kaufmann, 1996.
13. C. Gagné, M. Sebag, M. Schoenauer, and M. Tomassini. Ensemble learning for free with evolutionary algorithms? In Proceedings of the 9th annual conference on Genetic and evolutionary computation, pages 1782-1789. ACM, 2007.
14. L. Hansen and P. Salamon. Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(10):993-1001, 1990.
15. S. Hashem, B. Schmeiser, and Y. Yih. Optimal linear combinations of neural networks. Neural Networks, 10:599-614, 1997.
16. T. K. Ho. The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell., 20(8):832-844, 1998.
17. J. Holland. Escaping brittleness: the possibilities of general purpose learning algorithms applied to parallel rule-based systems. In Machine Learning: An Artificial Intelligence Approach, pages 129-138. Morgan Kauffman, 1986.
18. W. Jian, Y.-C. Xue, and J.-X. Qian. An improved particle swarm optimization algorithm with neighbo rhoods topologies. In Proceedings of 2004 International Conference on Machine Learning and Cybernetics, volume 4, pages 2332-2337, 2004.
19. J. Kennedy and R. C. Eberhart. Particle swarm optimization. In Proc. of the IEEE Int. Conf. on Neural Networks, pages 1942-1948, 1995.
20. H.-C. Kim, S. Pang, H.-M. Je, D. Kim, and S. Y. Bang. Constructing support vector machine ensemble. Pattern Recognition, 36(12):2757-2767, 2003.
21. L. Knight and S. Sen. Please: a prototype learning system using genetic algorithms. In Proceedings of the Sixth International Conference on Genetic Algorithms, pages 429-435. Morgan Kaufmann, 1995.
22. R. Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. In International Joint Conference on Artificial Intelligence (IJCAI), pages 1137-1145, 1995.
23. A. Krogh and J. Vedelsby. Neural network ensembles, cross validation, and active learning. In Advances in Neural Information Processing Systems, pages 231-238, 1995.
24. L. I. Kuncheva and L. C. Jain. Designing classifier fusion systems by genetic algorithms. IEEE Transactions On Evolutionary Computation, 4:327-336, 2000.
25. L. Lam and S. Suen. Application of majority voting to pattern recognition: an analysis of its behavior and performance. IEEE Transactions on Systems, Man and Cybernetics, Part A, 27(5):553-568, 1997.
26. Y. Liu and X. Yao. Ensemble learning via negative correlation. Neural Networks, 12:1399-1404, 1999.
27. C. Maurice. The swarm and queen: towards a deterministic and adaptive particle swarm optimization. In IEEE Congress on Evolutionary Computation, volume 2, pages 1951-1957, 1999.
28. B. Minaei-Bidgoli, G. Kortemeyer, and W. F. Punch. Optimizing classification ensembles via a genetic algorithm for a web-based educational system. In Lecture Notes in Computer Science, volume 3138, pages 397-406, 2004.
29. D. Opitz and R. Maclin. Popular ensemble methods: an empirical study. Journal of Artificial Intelligence Research, 11:169-198, 1999.
30. D. W. Opitz and J. W. Shavlik. A genetic algorithm approach for creating neural network ensembles. In Combining Artificial Neural Nets, pages 79-99. Springer-Verlag, 1999.
31. B. E. Rosen. Ensemble learning using decorrelated neural networks. Connection Science, 8:373-384, 1996.
32. S. F. Smith. A learning system based on genetic adaptive algorithms. PhD thesis, University of Pittsburgh, USA, 1980.
33. J. Trejos-Zelaya and M. Villalobos-Arias. Partitioning by particle swarm optimization. In P. Brito, et al., editors, Selected Contributions in Data Analysis and Classification. Studies in Classification, Data Analysis, and Knowledge Organization, chapter 22, pages 235-244. Springer, 2007.
34. L. Xu, A. Krzyzak, and C. Suen. Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on Systems, Man and Cybernetics, 22(3):418-435, 1992.
35. X. Yao and Y. Liu. Making use of population information in evolutionary artificial neural networks. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 28:417-425, 1998.
36. Z.-H. Zhou, J.-X. Wu, Y. Jiang, and S.-F. Chen. Genetic algorithm based selective neural network ensemble. In Proc. of IJCAI, pages 797-802, 2001.