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) .
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© 2000 Springer-Verlag Berlin Heidelberg
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Kuncheva, L.I. (2000). Non if-then fuzzy models. In: Fuzzy Classifier Design. Studies in Fuzziness and Soft Computing, vol 49. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1850-5_7
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DOI: https://doi.org/10.1007/978-3-7908-1850-5_7
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2472-8
Online ISBN: 978-3-7908-1850-5
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