Gas identification using density models

Sofiane Brahim-Belhouari, Amine Bermak

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)699-706
Number of pages8
JournalPattern Recognition Letters
Volume26
Issue number6
DOIs
Publication statusPublished - 1 May 2005
Externally publishedYes

Fingerprint

Identification (control systems)
Gases
Sensor arrays
Chemical sensors
Classifiers
Sensors
Experiments

Keywords

  • Classification
  • Gas sensor array
  • Mixture models
  • Pattern recognition

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Gas identification using density models. / Brahim-Belhouari, Sofiane; Bermak, Amine.

In: Pattern Recognition Letters, Vol. 26, No. 6, 01.05.2005, p. 699-706.

Research output: Contribution to journalArticle

Brahim-Belhouari, Sofiane ; Bermak, Amine. / Gas identification using density models. In: Pattern Recognition Letters. 2005 ; Vol. 26, No. 6. pp. 699-706.
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