Fuzzy ARTMAP dynamic decay adjustment: An improved fuzzy ARTMAP model with a conflict resolving facility

Applied Soft Computing Journal

Volumn 8, Issue 1, 2008, Pages 543-554

  Tan, Shing Chiang ^a   Rao, M V C ^a   Lim, Chee Peng ^b  

^a MULTIMEDIA UNIVERSITY   (Malaysia)

^b SCHOOL OF PHYSICS   (Malaysia)

Author keywords

Adaptive resonance theory; Classification; Dynamic decay adjustment; Fuzzy ARTMAP

Indexed keywords

ADAPTIVE SYSTEMS; LEARNING SYSTEMS; POWER GENERATION; RESONANCE; SOFTWARE PROTOTYPING;

ARTMAP MODEL; CIRCULATING WATER SYSTEM; CONFLICT-RESOLVING CLASSIFIER; DYNAMIC DECAY ADJUSTMENT (DDA) ALGORITHM; FUZZY ARTMAP;

FUZZY NEURAL NETWORKS;

EID: 34548487767     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2007.03.006     Document Type: Article

References (63)

1. Cover T.M., and Hart P.E. Nearest neighbor pattern classification. IEEE Trans. Inform. Theory IT 13 (1965) 21-27
2. Duda R.O., and Hart P.E. Pattern Classification and Scene Analysis (1973), Wiley, New York
3. Mack Y.P. Local properties of k-nearest neighbor regression estimates. SIAM J. Algeb. Discr. Methods 2 (1981) 311-323
4. In: Dasarathy B.V. (Ed). Nearest Neighbor Pattern Classification Techniques (1991), IEEE Comp. Soc. Press, Los Alamitos, CA
5. Akaike H. An approximation to the density function. Ann. Inst. Statist. Math. 6 (1954) 127-132
6. Parzen E. On the estimation of a probability density function and the mode. Ann. Math. Statist. 33 (1962) 1065-1076
7. Rosenblatt M. Remarks on some nonparametric estimates of a density function. Ann. Math. Statist. 27 (1956) 832-837
8. Quinlan J.R. Induction of decision trees. Mach. Learn. 1 (1986) 81-106
9. Quinlan J.R. C. 45: Programs for Machine Learning (1993), Morgan Kauffman, Los Altos, CA
10. Heckerman D., and Wellman M.P. Bayesian networks. Comm. ACM 38 (1995) 27-30
11. Friedman N., Geiger D., and Goldszmidt M. Bayesian network classifiers. Mach. Learn. 29 (1997) 131-163
12. Moody M.J., and Darken C.J. Fast learning in networks of locally-tuned processing units. Neural Comput. 1 (1989) 281-294
13. Kohonen T. Self-Organization and Associative Memory (1988), Springer, Berlin
14. Giles G.L., and Maxwell T. Learning, invariances, and generalization in high-order neural networks. Appl. Opt. 26 (1987) 4972-4978
15. Carpenter G.A., and Grossberg S. A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput. Vision Graph. Image Process. 37 (1987) 54-115
16. Carpenter G.A., and Grossberg S. Stable self-organisation of pattern recognition codes for analog input patterns. Appl. Opt. 26 (1987) 4919-4930
17. Specht D.F. Probabilistic neural networks. Neural Networks 3 (1990) 109-118
18. Rumelhalt D.E., Hinton G.E., and Williams R.J. Learning internal representations by error propagation. In: Rumelhalt D.E., and McCleland J.L. (Eds). Parallel Distributed Processing vol. 1 (1986), MIT Press, Cambridge, MA 318-362
19. Wu S., and Chow T.W.S. Induction machine fault detection using som-based rbf neural networks. IEEE Trans. Ind. Electronics 51 (2004) 183-194
20. Lopes M.L.M., Minussi C.R., and Lotufo A.D.P. Electric load forecasting using a fuzzy ART&ARTMAP neural network. Appl. Soft Comput. 5 (2005) 235-244
21. Moshou D., Hostens I., Papaioannou G., and Ramon H. Dynamic muscle fatigue detection using self-organizing maps. Appl. Soft Comput. 5 (2005) 391-398
22. Gupta C.N., Swaminathan R.P., and Krishnan S.M. Neural network classification of homomorphic segmented heart sounds. Appl. Soft Comput. 7 (2007) 286-297
23. Carpenter G.A., and Grossberg S. The art of adaptive pattern recognition by a self-organizing neural network. Computer 21 (1988) 77-88
24. Carpenter G.A., Grossberg S., and Reynolds J. ARTMAP: supervised real-learning and classification of nonstationary data by a self-organizing neural network. Neural Networks 4 (1991) 565-588
25. Carpenter G.A., Grossberg S., and Rosen D. Fuzzy art: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks 4 (1991) 759-771
26. Carpenter G.A., Grossberg S., Markuzon N., Reynolds J., and Rosen D. Fuzzy artmap: a neural network architecture for incremental learning of analog multidimensional maps. IEEE Trans. Neural Networks 3 (1992) 698-713
27. Carpenter G.A., and Ross W. ART-EMAP: a neural network architecture for object recognition by evidence accumulation. IEEE Trans. Neural Networks 6 (1995) 805-818
28. Carpenter G.A. Distributed learning, recognition, and prediction by art and artmap neural networks. Neural Networks 10 (1997) 1473-1494
29. Huber K.-P., and Berthold M.R. Building precise classifiers with automatic rule extraction. Proceedings of the IEEE International Conference Neural Networks, vol. 3 (1995) 1263-1268
30. Tan S.C., and Lim C.P. Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant. IEEE Trans. Energy Convers. 19 (2004) 369-377
31. Simpson P.K. Fuzzy min-max neural networks. I. Classification. IEEE Trans. Neural Networks 3 (1992) 776-786
32. Gabrys B., and Bargiela A. General fuzzy min-max neural network for clustering and classification. IEEE Trans. Neural Networks 11 (2000) 769-783
33. Rizzi A., Panella M., and Mascioli F.M.F. Adaptive resolution min-max classifiers. IEEE Trans. Neural Networks 13 (2002) 402-414
34. Berthold M.R., and Diamond J. Constructive training of probabilistic neural networks. Neurocomputing 19 (1998) 167-183
35. Reill D.L., Cooper L.N., and Elbaum C. A neural model for category learning. Biol. Cybern. 45 (1982) 35-41
36. Lim C.P., and Harrison R.F. An incremental adaptive network for on-line supervised learning and probability estimation. Neural Networks 10 (1997) 925-939
37. Fisher R.A. The use of multiple measurements in taxonomic problem. Ann. Eugenics 7 (1936) 179-188
38. Mangasarian O.L., and Wolberg W.H. Cancer diagnosis via linear programming. SIAM News 23 (1990) 1-18
39. S. Hettich, C.L. Blake, C.J. Merz, UCI repository of machine learning databases, 1998. [http://www.ics.uci.edu/∼mlearn/MLRepository.html], University of California, Department of Information and Computer Science, Irvine, CA.
40. Liu H., and Motoda H. Feature Selection for Knowledge Discovery and Data Mining (1998), Kluwer Academic, Boston
41. Efron B. Bootstrap methods: another look at the jackknife. Ann. Statist. 7 (1979) 1-26
42. Last M., and Maimon O. A compact and accurate model for classification. IEEE Trans. Knowl. Data Eng. 16 (2004) 203-215
43. Castellano G., Fanelli A.M., and Mencar C. An empirical risk functional to improve learning in a neuro-fuzzy classifier. IEEE Trans. Syst. Man Cybern. 34 (2004) 725-731
44. Ledezma A., Aler R., Sanchis A., and Borrajo D. Empirical evaluation of optimized stacking configurations. Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICRA 2004) (2004) 49-55
45. Kosinov S., Marchand-Maillet S.M., and Pun T. Visual object categorization using distance-based discriminant analysis. IEEE International Conference on Computer Vision and Pattern Recognition Workshop (2004)
46. Riquelme J.C., Aguilar-Ruiz J.S., and Valle C.D. Supervised learning by means of accuracy-aware evolutionary algorithms. Informat. Sci. 156 (2003) 173-188
47. Kurgan L.A., and Clos K.J. CAIM discretization algorithm. IEEE Trans. Knowl. Data Eng. 16 (2004) 145-153
48. Hong T.P., and Chen J.-B. Processing individual fuzzy attributes for fuzzy rule induction. Fuzzy Set Syst. 112 (2000) 127-140
49. Berzal F., Cubero J.-C., and Marín N. Building multi-way decision trees with numerical attributes. Informat. Sci. 165 (2004) 73-90
50. Cheang S.M., Lee K.H., and Leung K.S. Evolving data classification programs using genetic parallel programming. The 2003 Congress on Evolutionary Computation CEC'03, vol. 1 (2003) 248-255
51. http://www.phys.uni.torun.pl/kmk/projects/datasets.html.
52. Yen C.-W., Young C.-N., and Nagurka M.-L. A vector quantization method for nearest neighbor classifier design. Pattern Recogn. Lett. 25 (2004) 725-731
53. Ster B., Dobnikal A., et al. Neural networks in medical diagnosis: comparison with other methods. In: Bulsari A., et al. (Ed). Proceedings of the International Conference EANN'96 (1996) 427-430
54. Zhang N.L., Nielsen T.D., and Jensen F.V. Latent variable discovery in classification models. Artif. Intell. Med. 30 (2004) 283-299
55. Nauck D., and Kruse R. Obtaining interpretable fuzzy classification rules from medical data. Artif. Intell. Med. 16 (1999) 149-169
56. Li X.-B., Sweigart J.R., Teng J.T.C., Donohue J.M., Thombs L.A., and Wang S.M. Multivariate decision trees using linear discriminants and tabu search. IEEE Trans. Syst. Man Cybern. Part A 33 (2003) 194-205
57. Michie D., Spiegelhalter D., and Taylor C. Machine Learning, Neural and Statistical Classification (1994), Prentice Hall, Upper Sadle River, NJ
58. Carpenter G.A., and Tan A.H. Rule extraction: from neural architecture to symbolic representation. Connect. Sci. 7 (1995) 3-27
59. TNB Malaysia, System Description and Operating Procedures, Prai Power Station Stage 3, vol. 14, 1999.
60. Kohavi R. A study of cross validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th International Joint Conference Artificial Intelligence (IJCAI). Morgan Kaufmann (1995) 1137-1145
61. Lam W., Keung C.-K., and Liu D. Discovering useful concept prototypes for classification based on filtering and abstraction. IEEE Trans. Pattern Anal. Mach. Intell. 24 (2002) 1075-1090
62. Shi D., Yeung D.S., and Gao J. Sensitivity analysis applied to the construction of radial basis function networks. Neural Networks 18 (2005) 951-957
63. Wang D. Fast constructive-covering algorithm for neural networks and its implement in classification. Appl. Soft Comput. J. 8 (2007) 166-173