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Non if-then fuzzy models

  • Ludmila I. Kuncheva
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 49)

Abstract

In Chapter 1 we adopted Definition 1 stating that a fuzzy classifier is any classifier which uses fuzzy sets either during its training or during its operation. So, fuzzy classifier modeling stretches beyond fuzzy if-then designs discussed in the previous two chapters. This chapter presents non-if-then fuzzy models. These models can be grouped in different ways (see [39, 81, 115, 118, 273, 320]) . However, the boundaries between these groups are not sharp because many of the classification schemes can be assigned to more than one group (see, e.g., [232] where the authors use multiple rule-based prototypes and call their method a knowledge-oriented fuzzy k-nearest neighbor classifier) .

Keywords

Fuzzy Number Discriminant Function Class Label Fuzzy Relation Kernel Discriminant Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Ludmila I. Kuncheva
    • 1
  1. 1.School of InformaticsUniversity of WalesBangor GwyneddUK

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