A two-step fuzzy-Bayesian classification for high dimensional data

The goal of this paper is twofold. First, we present a supervised fuzzy c-mean (SFCM) classifier for the classification of high dimensional data. Comparisons of the conventional FCM clustering technique and Bayesian classification technique are also presented. Next, we present a two-step classifier...

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Main Authors: Mostafa, M.G.-H., Perkins, T.C., Farag, A.A.
Format: Conference Proceeding
Language:English
Subjects:
Bayesian methods
Classification algorithms
Clustering algorithms
Fuzzy logic
High-resolution imaging
Hyperspectral imaging
Hyperspectral sensors
Image segmentation
Remote sensing
Uncertainty
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Perkins, T.C.
Farag, A.A.
description The goal of this paper is twofold. First, we present a supervised fuzzy c-mean (SFCM) classifier for the classification of high dimensional data. Comparisons of the conventional FCM clustering technique and Bayesian classification technique are also presented. Next, we present a two-step classifier in which the proposed SFCM and Bayesian algorithms are used in a cooperative way such that classification results of the SFCM algorithm are used to compute the prior probabilities required for the Bayesian classifier. Classification results of the three algorithms are presented on simulated and real remote sensing multispectral data. The results obtained show improvements in the classification accuracy and reliability using the two-step algorithm.
doi_str_mv 10.1109/ICPR.2000.903573
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subjects Bayesian methods
Classification algorithms
Clustering algorithms
Fuzzy logic
High-resolution imaging
Hyperspectral imaging
Hyperspectral sensors
Image segmentation
Remote sensing
Uncertainty
title A two-step fuzzy-Bayesian classification for high dimensional data
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