Gas identification using density models

Sofiane Brahim-Belhouari, Amine Bermak

Research output: Contribution to journalArticle

21 Citations (Scopus)


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
Issue number6
Publication statusPublished - 1 May 2005



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

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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