Ensembles of ARTMAP-based neural networks: An experimental study

Applied Intelligence

Volumn 35, Issue 1, 2011, Pages 1-17

  Canuto, Anne M P ^a   Santos, Araken M ^a   Vargas, Rogério R ^a  

^a FEDERAL UNIVERSITY OF RIO GRANDE DO NORTE   (Brazil)

Author keywords

ARTMAP based neural networks; Ensemble systems; Feature selection

Indexed keywords

ARTMAP-BASED NEURAL NETWORKS; ENSEMBLE SYSTEMS; EXPERIMENTAL STUDIES; LEARNING PROCEDURES; LEARNING TIME; MULTI LAYER PERCEPTRON;

FEATURE EXTRACTION;

NEURAL NETWORKS;

EID: 79959374923     PISSN: 0924669X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10489-009-0199-2     Document Type: Article

References (41)

1. RE Banfield LO Hall KW Bowyer WP Kegelmeyer 2005 Ensemble diversity measures and their application to thinning Inf Fusion 6 1 49 62 10.1016/j.inffus.2004.04.005
2. Barbu C, Zhang K, Peng J, Buckles B (2005) Boosting in classifier fusion vs. fusing boosted classifiers. In: Proceedings of IEEE international conference of information reuse and integration, pp 332-337 (Pubitemid 43996569)
3. Blake CL, Merz CJ (1998) 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
4. Bertolami R, Bunke H (2006) Diversity analysis for ensembles of word sequence recognisers. In: Proceedings of 11th international workshop on structural and syntatic pattern recognition, pp 677-686 (Pubitemid 44543159)
5. G Brown J Wyatt R Harris X Yao 2004 Diversity creation methods: a survey and categorisation J Inf Fusion 6 1 5 20 10.1016/j.inffus.2004.04.004
6. Canuto A (2001) Combining neural networks and fuzzy logic for applications in character recognition. PhD thesis, University of Kent
7. A Canuto M Abreu LM Oliveira JC Xavier Jr A Santos 2007 Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles Pattern Recognit Lett 28 472 486 10.1016/j.patrec.2006.09.001 (Pubitemid 44918135)
8. A Canuto M Fairhurst G Howells 2001 Improving artmap learning through variable vigilance Int J Neural Syst 11 6 509 522
9. A Canuto G Howells M Fairhurst 2000 An investigation of the effects of variable vigilance within the repart neuro-fuzzy network J Intell Robot Syst 29 4 317 334 1007.68645 10.1023/A:1008159908688 Special issue on Neural-Fuzzy Systems
10. Canuto A, Santos A (2004) A comparative investigation of the RePART neural network in pattern recognition tasks. In: Proceedings of IEEE international joint conference on neural networks
11. Santos A, Canuto AM (2008) Investigating the influence of RePART in ensemble systems designed by boosting. In: Proceedings of IEEE international joint conference on neural networks
12. GA Carpenter S Grossberg 1991 A massively parallel architecture for a self-organizing neural pattern recognition machine Comput Vis Graph Image Process 37 54 115 10.1016/S0734-189X(87)80014-2
13. G Carpenter S Grossberg JH Reynolds 1991 Artmap: supervised real-time learning and classifcation of nonstationary data by a self-organizing neural network Neural Netw 4 565 588 10.1016/0893-6080(91)90012-T (Pubitemid 21710141)
14. G Carpenter S Grossberg M Markunzo JH Reynolds DB Rosen 1992 Fuzzy artmap: a neural network architecture for incremental supervised learning of analog multidimensional maps IEEE Trans Neural Netw 3 698 713 10.1109/72.159059 (Pubitemid 23555782)
15. G Carpenter N Markuzon 1998 Artmap-IC and medical diagnosis: instance counting and inconsistent cases Neural Netw 11 323 336 10.1016/S0893-6080(97) 00067-1 (Pubitemid 28231477)
16. Czyz J, Sadeghi M, Kittler J, Vandendorpe L (2004) Decision fusion for face authentication. In: Proceedings of the first international conference on biometric authentication, pp 686-693
17. J Demsar 2006 Statistical comparisons of classifiers over multiple data sets J Mach Learn Res 7 1 30 2274360 (Pubitemid 43022939)
18. Gal-Or M, May JH, Spangler WE (2005) Design methods using decision tree models and diversity measures in the selection of ensemble classification models. In: Multiple classifier systems: 6th international workshop. Lecture notes in computer science, pp 186-195 (Pubitemid 41422617)
19. G Giacinto F Roli 2001 Dynamic classifier selection based on multiple classifier behaviour Pattern Recognit 34 9 1879 1881 0995.68100 10.1016/S0031-3203(00)00150-3
20. Haykin S (1998) Neural networks, a comprehensive foundation, 2nd ed. Prentice Hall, New York
21. Kuncheva LI (2004) Combining pattern classifiers. Methods and algorithms. Wiley, New York
22. L Kuncheva M Skurichina R Duin 2002 An experimental study on diversity for bagging and boosting with linear classifier Inf Fusion 3 2 245 258 10.1016/S1566-2535(02)00093-3
23. Kuncheva L, Whitaker CJ (2002) Using diversity with three variants of boosting: aggressive, conservative, and inverse. In: Multiple classifier systems. Lecture notes in computer science, pp 81-90
24. L Kuncheva C Whitaker 2003 Measures of diversity in classifier ensembles Mach Learn 51 181 207 1027.68113 10.1023/A:1022859003006
25. R Ranawana V Palade 2006 Multi classifier systems-a review and roadmap for developers J Hybrid Intell Syst 3 1 35 61 1098.68690
26. Ruspini E, Bonissone P, Pedryez W (1998) Handbook of fuzzy computation. IOP, Bristol
27. Sharkey AJC (1999) Multi-net system. In: Sharkey AJC (ed) Combining artificial neural nets: ensemble and modular multi-net systems. Springer, Berlin, pp 1-30
28. C Spearman 1904 The proof and measurement of association between two things Am J Psychol 15 72 101 10.2307/1412159
29. E Tang P Suganthan X Yao 2006 An analysis of diversity measures Mach Learn 65 1 247 271 10.1007/s10994-006-9449-2 (Pubitemid 44451200)
30. Tian Q, Yu J, Huang TS (2005) Boosting multiple classifiers constructed by hybrid discriminant analysis. In: Lecture notes in computer science, pp 42-52 (Pubitemid 41422603)
31. Tsymbal A, Pechenizkiy M, Cunningham P (2003) Diversity in ensemble feature selection. Technical report, Trinity College, Dublin
32. T Windeatt 2005 Diversity measures for multiple classifier system analysis and design Inf Fusion 6 1 21 36 10.1016/j.inffus.2004.04.002
33. L Zadeh 1965 Fuzzy sets Inf Cont 8 338 353 219427 0139.24606 10.1016/S0019-9958(65)90241-X
34. G Carpenter S Grossberg M Markunzo JH Reynolds DB Rosen 1991 Fuzzy art: fats stable learning and categorization of analogpatterns by an adaptive resonance system Neural Netw 4 759 771 10.1016/0893-6080(91)90056-B
35. A Chebira E Bouyoucef M Rybnik K Madani 2006 ATNS: an adaptive tree neural structure Int J Inf Technol Intell Comput 1 3 463 476
36. Dietterich TG (2000) Ensemble methods in machine learning. In: Kittler J, Roli F (eds) Multiple classifier systems. Lecture notes in computer science, vol 1857. Springer, Berlin, pp 1-15
37. Ko AH-.R, Sabourin R, Britto AS (2006) Combining diversity and classification accuracy for ensemble selection in random subspaces. In: International joint conference on neural networks (IJCNN), pp 2144-2151 (Pubitemid 351369591)
38. K Madani L Thiaw 2007 Self-organizing multi-modeling: a different way to design intelligent predictors Neurocomputing 70 1618 2836 2852 10.1016/j.neucom.2006.06.016 (Pubitemid 47321598)
39. Platt J (1998) Machines using sequential minimal optimization. In: B. Schoelkopf, C. Burges, A. Smola (eds), Advances in kernel methods-support vector learning
40. Rybnik M, Chabira A, Madani K (2003) Auto-adaptive neural network tree structure based on complexity estimator. In: Computational methods in neural modeling. Lecture notes in computer science, vol 2686, pp 558-565
41. AMP Canuto L Santana M Abreu J Xavier Jr 2008 An analysis of data distribution in the ClassAge system: An agent-based system for classification tasks Neurocomputing (Amsterdam) 71 3319 3325