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Conformal Prediction for Distribution-Independent Anomaly Detection in Streaming Vessel Data
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. (SAIL)
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. (SAIL)
2010 (English)In: StreamKDD '10: Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques, Association for Computing Machinery (ACM), 2010, p. 47-55Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a novel application of the theory of conformal prediction for distribution-independent on-line learning and anomaly detection. We exploit the fact that conformal predictors give valid prediction sets at specified confidence levels under the relatively weak assumption that the (normal) training data together with (normal) observations to be predicted have been generated from the same distribution. If the actual observation is not included in the possibly empty prediction set, it is classified as anomalous at the corresponding significance level. Interpreting the significance level as an upper bound of the probability that a normal observation is mistakenly classified as anomalous, we can conveniently adjust the sensitivity to anomalies while controlling the rate of false alarms without having to find any application specific thresholds. The proposed method has been evaluated in the domain of sea surveillance using recorded data assumed to be normal. The validity of the prediction sets is justified by the empirical error rate which is just below the significance level. In addition, experiments with simulated anomalous data indicate that anomaly detection sensitivity is superior to that of two previously proposed methods.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2010. p. 47-55
Keywords [en]
Anomaly detection, conformal prediction, sea surveillance
National Category
Computer and Information Sciences
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-4656DOI: 10.1145/1833280.1833287Scopus ID: 2-s2.0-77956247958ISBN: 978-1-4503-0226-5 OAI: oai:DiVA.org:his-4656DiVA, id: diva2:392598
Conference
1st International Workshop on Novel Data Stream Pattern Mining Techniques, StreamKDD'10, Held in Conjunction with the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining; Washington, DC; 25 July 2010 through 25 July 2010
Available from: 2011-01-27 Created: 2011-01-27 Last updated: 2018-01-12Bibliographically approved

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Laxhammar, RikardFalkman, Göran

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf