Journal article : Review
A review of novelty detection
- Abstract:
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Publisher copy:
- 10.1016/j.sigpro.2013.12.026
Authors
+ National Institute for Health Research
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- Funder identifier:
- https://ror.org/0187kwz08
- Funding agency for:
- Clifton, L
- Grant:
- Biomedical Research Centre Programme, Oxford
+ Engineering and Physical Sciences Research Council
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- Funder identifier:
- https://ror.org/0439y7842
- Funding agency for:
- Clifton, DA
- Grant:
- WT 088877/Z/09/Z
+ Wellcome Trust
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- Funder identifier:
- https://ror.org/029chgv08
- Funding agency for:
- Clifton, DA
- Grant:
- WT 088877/Z/09/Z
+ Royal Academy of Engineering
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- Funder identifier:
- https://ror.org/0526snb40
- Funding agency for:
- Clifton, DA
- Grant:
- WT 088877/Z/09/Z
+ Fundação para a Ciência e Tecnologia
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- Funder identifier:
- https://ror.org/00snfqn58
- Funding agency for:
- Pimentel, MAF
- Grant:
- SFRH/BD/79799/2011
- Publisher:
- Elsevier
- Journal:
- Signal Processing More from this journal
- Volume:
- 99
- Pages:
- 215-249
- Publication date:
- 2014-01-02
- Acceptance date:
- 2013-12-23
- DOI:
- EISSN:
-
1872-7557
- ISSN:
-
0165-1684
- Language:
-
English
- Keywords:
- Subtype:
-
Review
- Pubs id:
-
pubs:449866
- UUID:
-
uuid:947eb5ef-ea04-4156-840a-ae957d35d4f6
- Local pid:
-
pubs:449866
- Source identifiers:
-
449866
- Deposit date:
-
2016-01-18
- ARK identifier:
Terms of use
- Copyright holder:
- Pimentel et al
- Copyright date:
- 2014
- Rights statement:
- © 2014 Published by Elsevier B.V.
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