Novelty detection applied to the classification problem using Probabilistic Neural Network

A novel pattern is an observation which is different as compared to the rest of the data. The task of novelty detection is to build a model which identifies novel patterns from a data set. This model has to be built in such a way that if a pattern is distant from the given training data, it should be classified as a novel pattern otherwise it should be classified into any one of the given classes. In this paper, we present two such new models, based on Probabilistic Neural Network for novelty detection. In the first model, we generate negative examples around the target class data and then train the classifier with these negative examples. In the second model, which is an incremental model, we present a new method to find optimal threshold for each class and if output value for a test pattern being assigned to a target class is less than the threshold of the target class, then we classify that pattern as a novel pattern. We show how decision boundaries are created when we add novelty detection mechanism and when we do not add novelty detection to our model. We show a comparative performance of both approaches.