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 method. We compare the six methods with two standard classifier designs: the 1nearest 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 errors) design. One of the competitive learning models produces the best minimal prototypes design (five prototypes that yield three resubstitution errors).