A comparison study of chemical sensor array pattern recognition algorithms

Abstract

Probabilistic neural networks (PNN), learning vector quantization (LVQ) neural networks, back-propagation artificial neural networks (BP-ANN), soft independent modeling of class analogy (SIMCA), Bayesian linear discriminant analysis (BLDA), Mahalanobis linear discriminant analysis (MLDA), and the nearest-neighbor (NN) pattern recognition algorithms are compared for their ability to classify chemical sensor array data. Comparisons are made based on five qualitative criteria (speed, training difficulty, memory requirements, robustness to outliers, and the ability to produce a measure of uncertainty) and one quantitative criterion (classification accuracy). Four sample data sets from our laboratory, involving simulated data and polymer-coated surface acoustic wave chemical sensor array data, are used to estimate classification accuracies for each method. Among the seven algorithms in this study and the four data sets, the neural network based algorithms (LVQ, PNN, and BP-ANN) have the highest classification accuracies. When considering the qualitative criteria, the LVQ and PNN approaches fare well compared to BP-ANN due to their simpler training methods. The PNN is recommended for applications where a confidence measure and fast training are critical, while speed and memory requirements are not. LVQ is suggested for all other applications of chemical sensor array pattern recognition.