Abstract
Prototype classifier is based on representing every cluster by a prototype. All the input patterns that belong to that cluster will have the same label as the prototype. It should be noted that a prototype does not have to be only one data. A cluster could be represented by more than one data. In this paper, the M-dimensional rectangle of the Fuzzy ART is used as a prototype. A new tree clustering structure replaces the training phase of Fuzzy ARTMAP. The obtained clusters are used to form the prototype rectangles. These rectangles will be used in the test phase of the Fuzzy ARTMAP. This algorithm is compared to the Nearest Neighbor classifier, the Fuzzy ARTMAP, C4.5, and the fuzzy ART-Var algorithms for different values of the vigilance parameter. Databases from the UCI repository will be used for comparison. Experimental results show the good generalization capability of this new algorithm.
Similar content being viewed by others
References
Bezdek, J, Pal, S (eds) (1992) Fuzzy models for pattern recognition: methods that search for structures in data. IEEE Press, New York
Buhmann MD (2003) Radial basis functions: theory and implementation. Cambridge University Press, Cambridge
Carpenter GA, Grossberg S, Rosen DB (1992) Fuzzy ART: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Netw 4: 759–771
Carpenter GA, Gjaja MN (1994) Fuzzy ART choice functions. In: Proceedings of the world congress on neural networks, pp 1713–1722, San Diego, CA
Carpenter GA, Grossberg S, Markuzon N, Reynolds JH, Rosen DB (1992) Fuzzy ARTMAP, a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans Neural Netw 3: 698–713
Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1): 21–27
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(4): 768–778
Dagher I, Georgiopoulos M, Heileman GL, Bebis G (1998) Fuzzy ARTVar: an improved fuzzy ARTMAP algorithm, IJCNN-98, vol 3, pp 1688–1693, Alaska, AK, 4–9 May 1998
Dudani SA (1976) The distance-weighted k-nearest-neighbor rule. IEEE Trans Syst Man Cybern 6: 325–327
Georgiopoulos M, Dagher I, Heileman GL, Bebis G (1999) Properties of learning of a fuzzy ART variant. Neural Netw 12(6): 837–850
Georgiopoulos M, Fernlund M, Bebis G, Heileman GL (1996) Order of search in fuzzy ART and fuzzy ARTMAP: a geometrical interpretation. In: Proceedings of the international conference on neural networks, pp 215–220, Washington, DC
Grossberg S (1976) Adaptive pattern recognition and universal recoding. II: Feedback, expectation, olfaction, and illusions. Biol Cybern 4(1): 9–20
Likas A, Vlassis N, Verbeek JJ (2003) The global k-means clustering algorithm. Pattern Recognit 36(2)
Quinlan JR (1996) Improved use of continuous attributes in c4.5. J Artif Intell Res 4: 77–90
Vapnik V (1995) The nature of statistical learning theory. Springer, Berlin
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Dagher, I. Fuzzy ART-based prototype classifier. Computing 92, 49–63 (2011). https://doi.org/10.1007/s00607-010-0130-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00607-010-0130-z