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Neural network classification and novelty detection

Novel data are data belonging to classes not included in the training set of the classifier. Neural network classifiers tend to put these data into the category of patterns that most resemble the novel ones, rather than label them as novel or unknown. This work investigates the ability of the back-propagation neural network architecture to detect novel patterns and concludes that this method is unsuitable for this task. It also explores the applicability of a different neural network architecture, the probabilistic neural network, and finds that this method shows superior performance as an overall classifier when compared to back-propagation and, in addition, is able to identify novel patterns.