Multiple-prototype classifier design

Authors:
J. C. Bezdek;T. R. Reichherzer;G. S. Lim;Y. Attikiouzel
Affiliations:
Div. of Comput. Sci., Univ. of West Florida, Pensacola, FL;-;-;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Year:
1998

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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)