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Multiple-prototype classifier design

Bezdek, J.C., Reichherzer, T.R., Lim, G.S. and Attikiouzel, Y. (1998) Multiple-prototype classifier design. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 28 (1). pp. 67-79.

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Link to Published Version: http://dx.doi.org/10.1109/5326.661091
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Abstract

Five methods that generate multiple prototypes from labeled data are reviewed. Then we introduce a new sixth approach, which is a modification of Chang's (1974) method. We compare the six methods with two standard classifier designs: the 1-nearest prototype (1-np) and 1-nearest neighbor (1-nn) rules. The standard of comparison is the resubstitution error rate; the data used are the Iris data. Our modified Chang's method produces the best consistent (zero-error) design. One of the competitive learning models produces the best minimal prototypes design (five prototypes that yield three resubstitution errors)

Publication Type: Journal Article
Publisher: IEEE
Copyright: © 1998 IEEE
URI: http://researchrepository.murdoch.edu.au/id/eprint/20387
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