A neural network classifier based on Dempster-Shafer theory

Abstract

A new adaptive pattern classifier based on the Dempster-Shafer theory of evidence is presented. This method uses reference patterns as items of evidence regarding the class membership of each input pattern under consideration. This evidence is represented by basic belief assignments (BBA) and pooled using the Dempster's rule of combination. This procedure can be implemented in a multilayer neural network with specific architecture consisting of one input layer, two hidden layers and one output layer. The weight vector, the receptive field and the class membership of each prototype are determined by minimizing the mean squared differences between the classifier outputs and target values. After training, the classifier computes for each input vector a BBA that provides a description of the uncertainty pertaining to the class of the current pattern, given the available evidence. This information may be used to implement various decision rules allowing for ambiguous pattern rejection and novelty detection. The outputs of several classifiers may also be combined in a sensor fusion context, yielding decision procedures which are very robust to sensor failures or changes in the system environment. Experiments with simulated and real data demonstrate the excellent performance of this classification scheme as compared to existing statistical and neural network techniques.