A study of parameter values for a Mahalanobis distance fuzzy classifier

Authors:
Peter J. Deer;Peter Eklund
Affiliations:
School of Information Technology, Griffith University, Parklands Drive, Southport, Queensland 9726, Australia;School of Information Technology and Electrical Engineering, The University of Queensland, St. Lucia Queensland 4072, Australia
Venue:
Fuzzy Sets and Systems - Data analysis
Year:
2003

Citing 4
Cited 2

Efficient Implementation of the Fuzzy c-Means Clustering Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergence theory for fuzzy c-means: counterexamples and repairs

IEEE Transactions on Systems, Man and Cybernetics
On the estimation of spatial-spectral mixing with classifier likelihood functions

Pattern Recognition Letters - Special issue on non-conventional pattern analysis in remote sensing
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms

Alternative adaptive fuzzy C-means clustering

EC'06 Proceedings of the 7th WSEAS International Conference on Evolutionary Computing
Environment recognition system for biped walking robot using vision based sensor fusion

IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems

Quantified Score

Hi-index	0.00

Visualization

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.