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Classification with multiple prototypes

Bezdek, J.C., Reichherzer, T.R., Lim, G. and Attikiouzel, Y. (1996) Classification with multiple prototypes. In: Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, 8 - 11 September, New Orleans, USA pp. 626-632.

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Abstract

We compare learning vector quantization, fuzzy learning vector quantization, and a deterministic scheme called the dog-rabbit (DR) model for generation of multiple prototypes from labeled data for classifier design. We also compare these three models to three other methods: a dumping method due to Chang (1974); our modification of Chang's method; and a derivative of the batch fuzzy c-means algorithm due to Yen-Chang (1994). All six methods are superior to the labeled subsample means, which yield 11 errors with 3 prototypes. Our modified Chang's method is, for the Iris data used in this study, the best of the six schemes in one sense; it finds 11 prototypes that yield a resubstitution error rate of 0. In a different sense, the DR method is best, yielding a classifier that commits only 3 errors with 5 prototypes.

Item Type: Conference Paper
Publisher: IEEE
Copyright: © 1996 IEEE
URI: http://researchrepository.murdoch.edu.au/id/eprint/19282
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