A unifying methodology for the evaluation of neural network models on novelty detection tasks

An important issue in data analysis and pattern classification is the detection of anomalous observations and its influence on the classifier's performance. In this paper, we introduce a novel methodology to systematically compare the performance of neural network (NN) methods applied to novelty detection problems. Initially, we describe the most common NN-based novelty detection techniques. Then we generalize to the supervised case, a recently proposed unsupervised novelty detection method for computing reliable decision thresholds. We illustrate how to use the proposed methodology to evaluate the performances of supervised and unsupervised NN-based novelty detectors on a real-world benchmarking data set, assessing their sensitivity to training parameters, such as data scaling, number of neurons, training epochs and size of the training set.