The effect of a non-exhaustively defined set of classes on neural network classifications

Freedom from assumptions about the data set used is one attraction of neural network classifiers. However, neural network classification is not assumption-free. It is typically assumed that the set of classes has been defined exhaustively. If this assumption is unsatisfied, cases of an untrained class will be present and commissioned into the set of trained classes to the detriment of classification accuracy, for both hard and soft classifications. This is illustrated with MLP and RBF neural networks together with suggestions of how to reduce the problem for both hard and soft image classifications.