The DOI matches its Crossref record; 311 of the 322 references it cites were found in a registry or an index.
A review of novelty detection
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In the citing paper
“This phenomenon introduces various uncertainties into the system's outcomes [4,5].”
From Software engineering challenges for machine learning applications: A literature review (2020) · cites this paper as [4]
Citations
Cites
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
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Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
Fast algorithms for mining association rules
Agrawal, R., & Srikant, R. (1998) · Cited by 11,362
On Estimation of a Probability Density Function and Mode
Parzen, E. (1962) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177704472 · Cited by 10,715
Kohonen, T. (1990) · Neurocomputing · doi:10.1016/s0925-2312(98)00030-7 · Cited by 10,686
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359
Pattern Recognition with Fuzzy Objective Function Algorithms
Bezdek, J. C. (1981) · doi:10.1007/978-1-4757-0450-1 · Cited by 9,881
Fast Algorithms for Mining Association Rules in Large Databases
Agrawal, R., & Srikant, R. (1994) · Very Large Data Bases · Cited by 9,454
Flagged first, then by citations
11 of the 322 references couldn’t be matched to a known paper.
Research works printed but not linked: 15
- H. Escalante, A comparison of outlier detection algorithms for machine learning, in: Proceedings of the International Conference on Communications in Computing, Citeseer, 2005.
- Marsland, 2003, Novelty detection in learning systems, Neural Comput. Surv., 3, 157
- Bishop, 2006, vol. 4
- Bartkowiak, 2011, Anomaly, novelty, one-class classification, Int. J. Comput. Inf. Syst. Ind. Manage. Appl., 3, 61
- Y. Gatsoulis, E. Kerr, J. Condell, N. Siddique, T. McGinnity, Novelty detection for cumulative learning, in: Proceedings of the Conference on Towards Autonomous Robotic Systems, 2010, pp. 62–67.
- E. Kerr, Y. Gatsoulis, N.H. Siddique, J.V. Condell, T.M. McGinnity, Brief overview of novelty detection methods for robotic cumulative learning, in: Proceedings of the 21st National Conference on Artificial Intelligence and Cognitive Science, 2010, pp. 171–180.
- McLachlan, 1988, vol. 1
- Embrechts, 1997, vol. 33
- Sugiyama, 2010, Density ratio estimation, RIMS Kokyuroku, 10
- Basseville, 1993, vol. 104
- Zhou, 2011, Unsupervised distributed novelty detection on scientific simulation data, J. Comput. Inf. Syst., 7, 1533
- Hassan, 2012, A heuristic approach for sensor network outlier detection, Int. J. Res. Rev. Wirel. Sensor Netw. (IJRRWSN), 1, 66
- Labib, 2002, NSOM, Netw. Secur., 1
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- W. Hu, Y. Liao, V. Vemuri, Robust anomaly detection using support vector machines, in: Proceedings of the International Conference on Machine Learning, 2003, pp. 282–289.
Cited by
Sorted by citations
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., & Langs, G. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-59050-9_12 · Cited by 2,627
MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection
Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00982 · Cited by 2,203
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks
Schlegl, T., Seeböck, P., Waldstein, S. M., Langs, G., & Schmidt-Erfurth, U. M. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.010 · Cited by 1,645
A Unifying Review of Deep and Shallow Anomaly Detection
Ruff, L., Kauffmann, J., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3052449 · Cited by 1,184
Recent Advances in Open Set Recognition: A Survey
Geng, C., Huang, S.-J., & Chen, S. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2981604 · Cited by 1,106
Metadata
- Authors
- Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Pimentel, Marco A.F.
- Venue
- Signal Processing
- Publisher
- Elsevier BV
- Published
- 2014-06
- Volume
- 99
- Pages
- 215-249
- ISSN
- 0165-1684 (ISSN-L)
- OpenAlex
- W2115627867
- Cited by
- 1,649
- References
- 322
- Citations by year
- 2026: 94 · 2025: 114 · 2024: 137 · 2023: 140 · 2022: 161 · 2021: 199 · 2020: 219 · 2019: 170 · 2018: 162 · 2017: 103 · 2016: 81 · 2015: 49 · 2014: 17
- FWCI
- 77.16
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Signal Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
- Keywords
- novelty detection, one-class classification, anomaly detection, critical systems
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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