The Multiclass Probabilistic Neural Network (PNN) Classifier

This paper describes a general multiclass classification algorithm called the Probabilistic Neural Network (PNN) (Specht, 1988). Its decision surfaces approach the Bayes- optimal boundaries by non-parametric probability density function (PDF) estimation as the number of training samples grow. Theoretical and practical aspects of the PNN classification method are discussed as well as its advanatages and disadvantages in comparison to the Backpropagation Network (BN). The algorithm has been implemented primarily as a research tool for feature selection and classification for general Pattern Recognition (PR) problems. It will also be used for classifier-directed signal processing tasks. The method has been successfully used to distinguish amongst the resonant sounds of five thin metal gongs of different regular shapes having the same areas and thicknesses. This example application includes a description of data sampling, primary analysis, feature selection and classification which can be usefully generalised to other similar classification problems. Some other PNN applications investigated by the author and others mentioned in the literature are described to show some of the PNN''sfeatures and uses. These include a Bayes-optimal maximum liklehood signal detector, ship hull classification from sonar signals and electrocardiogram classifications from QRX complexes.