A Study of Parameter Values For a Mahalanobis Distance Fuzzy Classifier

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Author(s)
Deer, Peter
Eklund, Peter
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Didier Dubois, Henri Prade
Date
2003
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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.

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Fuzzy Sets and Systems
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137
Issue
2
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Pure Mathematics
Artificial Intelligence and Image Processing
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