A study of parameter values for a Mahalanobis distance fuzzy classifier

Peter J Deer; Peter Eklund

A study of parameter values for a Mahalanobis distance fuzzy classifier

Peter J Deer, Peter Eklund

Publikation: Artikel i tidsskrift og konference artikel i tidsskrift › Tidsskriftartikel › Forskning › peer review

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.

Originalsprog	Udefineret/Ukendt
Tidsskrift	Fuzzy Sets and Systems
Vol/bind	137
Udgave nummer	2
Sider (fra-til)	191-213
Antal sider	23
ISSN	0165-0114
Status	Udgivet - 2003
Udgivet eksternt	Ja

Citationsformater

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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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AU - Deer, Peter J

AU - Eklund, Peter

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N2 - 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.

AB - 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.

KW - Fuzzy classification

KW - Image Processing

M3 - Tidsskriftartikel

SN - 0165-0114

VL - 137

SP - 191

EP - 213

JO - Fuzzy Sets and Systems

JF - Fuzzy Sets and Systems

IS - 2

ER -