Probabilistic Neural Networks for Chemical Sensor Array Pattern Recognition: Comparison Studies, Improvements and Automated Outlier Rejection

For application to chemical sensor arrays, the ideal pattern recognition is accurate, fast, simple to train, robust to outliers, has low memory requirements, and has the ability to produce a measure of classification certainty. In this work, four data sets representing typical chemical sensor array data were used to compare seven pattern recognition algorithms nearest neighbor, Mahalanobis linear discriminant analysis, Bayesian linear discriminant analysis, SIMCA, back propagation neural networks, probabilistic neural networks PNN, and learning vector quantization LVQ for their ability to meet the criteria. LVQ and PNN exhibited high classification accuracy and met many of the qualitative criteria for an ideal algorithm. Based on these results, a new algorithm LVQ-PNN that incorporates the best features of PNN and LVQ was developed. The LVQ-PNN algorithm was further improved by the addition of a faster training procedure. It was then compared with the other seven algorithms. The LVQ-PNN method achieved excellent classification performance. A general procedure for selecting the optimal rejection threshold for a PNN based algorithm using Monte Carlo simulations also was demonstrated. This outlier rejection strategy was implemented for an LVQ-PNN classifier and found consistently to reject ambiguous patterns.