Committee Machine with Over 95% Classification Accuracy for Combustible Gas Identification

Gas identification represents a big challenge for pattern recognition systems due to several particular problems such as non-selectivity and drift. This paper proposes a gas identification committee machine (CM), which combines various gas identification algorithms, to obtain a unified decision with improved accuracy. The CM combines 5 different classifiers: K nearest neighbors (KNN), multi-layer perceptron (MLP), radial basis function (RFB), Gaussian mixture model (GMM) and probabilistic PCA (PPCA). A data acquisition system using tin-oxide gas sensor array has been designed in order to create a real gas data set. The committee machine is implemented by assembling the outputs of these gas identification algorithms based on weighted combination rule. Experiments on real sensors´ data proved the effectiveness of our system with an improved accuracy 95.9% over the individual classifiers.