The use of coevolution and the artificial immune system for ensemble learning

Soft Computing

Volumn 15, Issue 9, 2011, Pages 1735-1747

  Barbosa, Bruno H G ^a,b,c   Bui, Lam T ^c   Abbass, Hussein A ^c   Aguirre, Luis A ^b   Braga, Antônio P ^b  

^a FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

^c FEDERAL UNIVERSITY OF LAVRAS   (Brazil)

Author keywords

Artificial immune system; Coevolution; Differential evolution; Ensemble; Neural networks; Size

Indexed keywords

ARTIFICIAL IMMUNE SYSTEM; CO-EVOLUTION; DIFFERENTIAL EVOLUTION; ENSEMBLE; SIZE;

EVOLUTIONARY ALGORITHMS; NEURAL NETWORKS;

IMMUNOLOGY;

EID: 80051670016     PISSN: 14327643     EISSN: 14337479     Source Type: Journal    
DOI: 10.1007/s00500-010-0613-z     Document Type: Article

References (51)

1. Abbass HA (2002) An evolutionary artificial neural networks approach for breast cancer diagnosis. Artif Intell Med 25(3): 265-281.
2. Abbass HA (2003a) Pareto neuro-evolution: constructing ensemble of neural networks using multi-objective optimization. Proc IEEE Congr Evol Comput 3: 2074-2080.
3. Abbass HA (2003b) Speeding up backpropagation using multiobjective evolutionary algorithms. Neural Comput 15(11): 2705-2726.
4. Barbosa BHG, Bui LT, Abbass HA, Aguirre LA, Braga AP (2008) Evolving an ensemble of neural networks using artificial immune systems. In: Proceedings of the 7th international conference on simulated evolution and learning. Lecture notes in computer science, Melbourne, vol 5361. Springer, Berlin, pp 121-130.
5. Breiman L (1996) Bagging predictors. Mach Learn 24(2): 123-140.
6. Brown G, Wyatt J, Harris R, Yao X (2005) Diversity creation methods: a survey and categorisation. J Inf Fusion 6(1): 5-20.
7. Brown G, Wyatt J, Tino P (2005) Managing diversity in regression ensembles. J Mach Learn Res 6: 1621-1650.
8. Castro PD, Coelho GP, Caetano MF, Zuben FJV (2005) Designing ensembles of fuzzy classification systems: an immune approach. Lect Notes Comput Sci 3627: 469-482.
9. Chakraborty UK (2008) Advances in differential evolution. Springer, Berlin.
10. Chandra A, Yao X (2006) Evolving hybrid ensembles of learning machines for better generalisation. Neurocomputing 69(7-9): 686-700.
11. Costa MA, Braga AP, Menezes BR (2003) Training neural networks with a multi-objective sliding mode control algorithm. Neurocomputing 51: 467-473.
12. Dasgupta D (ed) (2008) Artificial immune systems and their applications. Springer, Berlin.
13. de Castro LN, Timmis J (2002) Artificial immune systems: a new computational intelligence approach. Springer, London.
14. de Castro LN, Zuben FJV (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evol Comput 6(3): 239-251.
15. Dietterich TG (2002) Ensemble learning. In: Arbib MA (ed) The handbook of brain theory and neural networks, 2nd edn. The MIT Press, Cambridge.
16. Drucker H, Cortes C, Jackel LD, LeCun Y, Vapnik V (1994) Boosting and other ensemble methods. Neural Comput 6(6): 1289-1301.
17. Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19: 1-67.
18. Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38(4): 367-378.
19. García-Pedrajas N, Fyfe C (2007) Immune network based ensembles. Neurocomputing 70(7-9): 1155-1166.
20. García-Pedrajas N, Fyfe C (2008) Construction of classifier ensembles by means of artificial immune systems. J Heuristics 14(3): 285-310.
21. García-Pedrajas N, Ortiz-Boyer D (2007) A cooperative constructive method for neural networks for pattern recognition. Pattern Recognit 40(1): 80-98.
22. García-Pedrajas N, Hervßs-Martfnez C, Ortiz-Boyer D (2005) Cooperative coevolution of artificial neural network ensembles for pattern classification. IEEE Trans Evol Comput 9(3): 271-302.
23. Geman S, Bienenstock E, Doursat R (1992) Neural networks and the bias/variance dilemma. Neural Comput 4(1): 1-58.
24. Hansen LK, Salamon P (1990) Neural networks ensembles. IEEE Trans Pattern Anal Mach Intell 12(10): 993-1001.
25. Islam M, Yao X, Murase K (2003) A constructive algorithm for training cooperative neural network ensembles. IEEE Trans Neural Netw 14(4): 820-834.
26. Jin Y, Okabe T, Sendhoff B (2004) Neural network regularization and ensembling using multi-objective evolutionary algorithms. Proc IEEE Congr Evol Comput 1: 1-8.
27. Krogh A, Vedelsby J (1995) Neural network ensembles, cross validation, and active learning. In: Tesauro G, Touretzky DS, Leen TK (eds) Advances in neural information processing systems, vol 7. The MIT Press, Cambridge, pp 231-238.
28. Kuncheva LI, Whitaker CJ (2003) Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach Learn 51(2): 181-207.
29. Liu Y, Yao X (1999) Ensemble learning via negative correlation. Neural Netw 12(10): 1399-1404.
30. Liu Y, Yao X, Higuchi T (2000) Evolutionary ensembles with negative correlation learning. IEEE Trans Evol Comput 4(4): 380-387.
31. McKay R, Abbass HA (2001) Anti-correlation: a diversity promotion mechanisms in ensemble learning. Aust J Intell Inf Process Syst 7(3): 139-149.
32. Nguyen MH (2006) Cooperative coevolutionary mixture of experts: a neuro ensemble approach for automatic decomposition of classification problems. PhD thesis, School of Information Technology and Electrical Engineering, University of New South Wales.
33. Nguyen MH, Abbass HA, McKay RI (2005) Stopping criteria for ensemble of evolutionary artificial neural networks. Appl Soft Comput 6: 100-107.
34. Nguyen MH, Abbass HA, Mckay RI (2006) A novel mixture of experts model based on cooperative coevolution. Neurocomputing 70: 155-163.
35. Opitz D, Maclin R (1999) Popular ensemble methods: an empirical study. J Artif Intell Res 11: 169-198.
36. Panait L, Luke S, Wiegand RP (2006) Biasing coevolutionary search for optimal multiagent behaviors. IEEE Trans Evol Comput 10(6): 629-645.
37. Perrone MP, Cooper LN (1993) When networks disagree: ensemble methods for hybrid neural network. In: Mammone RJ (ed) Neural networks for speech and image processing. Chapman & Hall, London, pp 126-142.
38. Potter M (1997) The design and analysis of a computational model of cooperative coevolution. PhD thesis, George Mason University, Fairfax, Virginia.
39. Potter MA, Jong KAD (2000) Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol Comput 8(1): 1-29.
40. Rodriguez J, Kuncheva L, Alonso C (2006) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28(10): 1619-1630.
41. Schapire RE (1990) The strength of weak learnability. Mach Learn 5(2): 197-227.
42. Sollich P, Krogh A (1996) Learning with ensembles: how overfitting can be useful. In: Touretzky DS, Mozer MC, Hasselmo ME (eds) Advances in neural information processing systems, vol 8. The MIT Press, Cambridge, pp 190-196.
43. Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization. J Glob Optim 11: 341-359.
44. Ueda N, Nakano R (1996) Generalization error of ensemble estimators. In: IEEE international conference on neural networks, Washington, DC, USA, vol 1, pp 90-95.
45. Wiegand RP, Potter MA (2006) Robustness in cooperative coevolution. In: GECCO '06: proceedings of the 8th annual conference on genetic and evolutionary computation, New York, NY, USA. ACM Press, pp 369-376.
46. Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco.
47. Wolpert DH (1992) Stacked generalization. Neural Netw 5(2): 241-259.
48. Yao X (1999) Evolving artificial neural networks. Proc IEEE 87(9): 1423-1447.
49. Yao X, Islam MM (2008) Evolving artificial neural network ensembles. IEEE Comput Intell Mag 3(1): 31-42.
50. Zhang X, Wang S, Shan T, Jiao L (2005) Selective SVMs ensemble driven by immune clonal algorithm. Lect Notes Comput Sci 3449: 325-333.
51. Zhou ZH, Wu J, Tang W (2002) Ensembling neural networks: many could be better than all. Artif Intell 137(1-2): 239-263.