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. © 2014 Published by Elsevier B.V.

Original publication

DOI

10.1016/j.sigpro.2013.12.026

Type

Journal article

Journal

Signal Processing

Publication Date

01/06/2014

Volume

99

Pages

215 - 249