Abstract
Fuzzy ARTMAP (FAM), which is a supervised model from the adaptive resonance theory (ART) neural network family, is one of the conspicuous neural network classifier. The generalization/performance of FAM is affected by two important factors which are network parameters and presentation order of training data. In this paper we introduce a genetic algorithm to find a better presentation order of training data for FAM. The proposed method which is the combination of genetic algorithm with Fuzzy ARTMAP is called Genetic Ordered Fuzzy ARTMAP (GOFAM). To illustrate the effectiveness of GOFAM, several standard datasets from UCI repository of machine learning databases are experimented. The results are analyzed and compared with those from FAM and Ordered FAM which is used to determine a fixed order of training pattern presentation to FAM. Experimental results demonstrate the performance of GOFAM is much better than performance of Fuzzy ARTMAP and Ordered Fuzzy ARTMAP. In term of network size, GOFAM performs significantly better than FAM and Ordered FAM.
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References
Andonie R, Sasu L (2006) Fuzzy ARTMAP with input relevances. IEEE Trans Neural Netw 17: 929–941
Carpenter GA, Grossberg S, Markuzon N, Reynolds J, Rosen D (1992) Fuzzy ARTMAP: a neural network architecture for incremental learning of analog multidimensional maps. IEEE Trans Neural Netw 3: 698–713
Carpenter GA, Milenova B, Noeske B (1998) Distributed ARTMAP: a neural network for fast distributed supervised learning. Neural Netw 11: 793–813
Carpenter GA, Ross W (1995) ART-EMAP: a neural network architecture for learning and prediction by evidence accumulation. IEEE Trans Neural Netw 6: 805–818
Dagher I, Georgiopoulos M, Heileman G, Bebis G (1998) Fuzzy ARTVar: an improved fuzzy ARTMAP algorithm. In: Proceedings of IEEE world congress computational intelligence WCCI’98. pp 1688–1693
Dagher I, Georgiopoulos M, Heileman GL, Bebis G (1999) An ordering algorithm for pattern presentation in fuzzy ARTMAP that tends to improve generalization performance. IEEE Trans Neural Netw 10: 768–778
Gomez-Sanchez E, Dimitriadis Y, Cano-Izquierdo J, Lopez-Coronado J (2002) ARTMAP: use of mutual information for category reduction in fuzzy ARTMAP. IEEE Trans Neural Netw 13: 58–69
Guo GD, Li SZ (2003) Content-based audio classification and retrieval by support vector machines. IEEE Trans Neural Netw 14: 209–214
Hettich S, Blake CL, Merz CJ (1998) UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html. Department of Information and Computer Science, University of California, Irvine, CA
Ishibuchi H, Yamamoto T, Nakashima T (2005) Hybridization of fuzzy GBML approaches for pattern classification problems. IEEE Trans Syst Man Cybern B Cybern 35: 359–365
Lim CP, Harrison RF (1997) An incremental adaptive network for on-line supervised learning and probability estimation. Neural Netw 10: 925–939
Marriott S, Harrison RF (1995) A modified fuzzy ARTMAP architecture for the approximation of noisy mappings. Neural Netw 8: 619–641
Pernkopfa F (2005) Bayesian network classifiers versus selective k-NN classifier. Pattern Recognit 38: 1–10
Polikar R, Udpa L, Udpa SS, Honovar V (2001) Learn++: an incremental learning algorithm for supervised neural networks. IEEE Trans Syst Man Cybern C 31: 497–508
Roy A (2000) Artificial neural networks—a science in trouble. ACM SIGKDD Explor 1: 33–38
Simpson PK (1992) Fuzzy min–max neural networks—part 1: classification. IEEE Trans Neural Netw 3: 776–786
Verzi S, Heileman G, Georgiopoulos M, Healy M (1998) Boosted ARTMAP. In: Proceedings of IEEE world congress computational intelligence WCCI’98. pp 396–400
Vigdor B, Lerner B (2006) Accurate and fast off and online fuzzy ARTMAP-based image classification with application to genetic abnormality diagnosis. IEEE Trans Neural Netw 17: 1288–1300
Williamson J (1996) Gaussian ARTMAP: a neural network for fast incremental learning of noisy multidimensional maps. Neural Netw 9: 881–897
Wu Y, Ianakiev K, Govindaraju V (2002) Improved k-nearest neighbor classification. Pattern Recognit 35: 2311–2318
Zhang GP (2000) Neural networks for classification: a survey. IEEE Trans Syst Man Cybern C Appl Rev 30: 451–462
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Yaghini, M., Shadmani, M.A. GOFAM: a hybrid neural network classifier combining fuzzy ARTMAP and genetic algorithm. Artif Intell Rev 39, 183–193 (2013). https://doi.org/10.1007/s10462-011-9265-3
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DOI: https://doi.org/10.1007/s10462-011-9265-3