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A study of parameter values for a Mahalanobis Distance fuzzy classifier

Version 2 2024-06-04, 15:17
Version 1 2019-07-19, 12:32
journal contribution
posted on 2024-06-04, 15:17 authored by PJ Deer, Peter EklundPeter Eklund
A supervised Mahalanobis Distance fuzzy classifier (and the related fuzzy c-means clustering algorithm) requires the a priori selection of a weighting parameter called the fuzzy exponent. Guidance in the existing literature on an appropriate value is not definitive. This paper attempts to rigorously justify previous experimental findings on suitable values for this fuzzy exponent, using the criterion that fuzzy set memberships reflect class proportions in the mixed pixels of a remotely sensed image.

History

Journal

Fuzzy sets and systems

Volume

137

Pagination

191-213

Location

Amsterdam, The Netherlands

ISSN

0165-0114

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2002, Elsevier Science B.V.

Issue

2

Publisher

Elsevier

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