A review of novelty detection

Novelty detection is the task of classifying test data that differ in some respect from the data that are available during training. This may be seen as one-class classification, in which a model is constructed to describe normal training data. The novelty detection approach is typically used when the quantity of available abnormal data is insufficient to construct explicit models for non-normal classes. Application includes inference in datasets from critical systems, where the quantity of available normal data is very large, such that normality may be accurately modelled. In this review we aim to provide an updated and structured investigation of novelty detection research papers that have appeared in the machine learning literature during the last decade. (C) 2014 Published by Elsevier B.V.