Multiple-prototype classifier design

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)...

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Bibliographic Details
Published in:IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews man and cybernetics. Part C, Applications and reviews, 1998-02, Vol.28 (1), p.67-79
Main Authors: Bezdek, J.C., Reichherzer, T.R., Lim, G.S., Attikiouzel, Y.
Format: Article
Language:English
Subjects:
Computer science
Error analysis
Hypercubes
Iris
Marine vehicles
Nearest neighbor searches
Pattern recognition
Prototypes
Stock markets
Testing
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Summary: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).
ISSN:1094-6977
1558-2442
DOI:10.1109/5326.661091