Gas identification using density models

doi:10.1016/j.patrec.2004.09.020

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Gas identification using density models

2005-05-01

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S. Brahim-Belhouari

Amine Bermak

DOI：10.1016/j.patrec.2004.09.020

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Abstract

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In this paper we compare the accuracy of a range of advanced density models for gas identification from sensor array signals. Density estimation is applied in the construction of classifiers through the use of Bayes rule. Experiments on real sensors' data proved the effectiveness of the approach with an excellent classification performance. We compare the classification accuracy of four density models, Gaussian mixture models, Generative topographic mapping, Probabilistic PCA mixture and K nearest neighbors. On our gas sensors data, the best performance was achieved by Gaussian mixture models. (c) 2004 Elsevier B.V. All rights reserved.

Keywords:

classification

gas sensor array

mixture models

pattern recognition

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