Some Notes on Twenty One 21 Nearest Prototype Classifiers

  • James C. Bezdek
  • Ludmila I. Kuncheva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)

Abstract

Comparisons made in two studies of 21 methods for finding prototypes upon which to base the nearest prototype classifier are discussed. The criteria used to compare the methods are by whether they: (i) select or extract point prototypes; (ii) employ pre- or post-supervision; and (iii) specify the number of prototypes a priori, or obtain this number “automatically”. Numerical experiments with 5 data sets suggest that pre-supervised, extraction methods offer a better chance for success to the casual user than postsupervised, selection schemes. Our calculations also suggest that methods which find the “best” number of prototypes “automatically” are not superior to user specification of this parameter.

Keywords

Data condensation and editing Nearest neighbor classifiers Nearest prototype classifiers Post-supervision Pre-supervision 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • James C. Bezdek
    • 1
  • Ludmila I. Kuncheva
    • 2
  1. 1.Computer Science DepartmentUniversity of West FloridaPensacolaUSA
  2. 2.School of InformaticsUniversity of WalesBangorUK

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