A study of parameter values for a Mahalanobis Distance fuzzy classifier
Fuzzy Sets and SystemsPublished 30 December 2002
Peter Deer, Peter Eklund
Citations37
SJR quartileQ1
SJR score0.75
SNIP1.25
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TL;DR
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.
Abstract
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.
Keywords
Computer ScienceEngineering
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