Conference paper
Classification with multiple prototypes
Proceedings of IEEE 5th International Fuzzy Systems, Vol.1, pp.626-632
IEEE
Proceedings of the Fifth IEEE International Conference on Fuzzy Systems (New Orleans, USA, 08/09/1996–11/09/1996)
1996
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.
Details
- Title
- Classification with multiple prototypes
- Authors/Creators
- J.C. Bezdek (Author/Creator)T.R. Reichherzer (Author/Creator)G. Lim (Author/Creator)Y. Attikiouzel (Author/Creator)
- Publication Details
- Proceedings of IEEE 5th International Fuzzy Systems, Vol.1, pp.626-632
- Conference
- Proceedings of the Fifth IEEE International Conference on Fuzzy Systems (New Orleans, USA, 08/09/1996–11/09/1996)
- Publisher
- IEEE
- Identifiers
- 991005543915707891
- Copyright
- © 1996 IEEE
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Conference paper
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