A comparative study of density models for gas identification using microelectronic gas sensor

The aim of this paper is to 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 has 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 the Gaussian mixture models.