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A Probabilistic Approach to Aggregating Anomalies for Unsupervised Anomaly Detection with Industrial Applications
RISE, Swedish ICT, SICS. Decisions, Networks and Analytics lab.
RISE, Swedish ICT, SICS. Decisions, Networks and Analytics lab.
Number of Authors: 2
2015 (English)Conference paper, (Refereed)
Abstract [en]

This paper presents a novel, unsupervised approach to detecting anomalies at the collective level. The method probabilistically aggregates the contribution of the individual anomalies in order to detect significantly anomalous groups of cases. The approach is unsupervised in that as only input, it uses a list of cases ranked according to its individual anomaly score. Thus, any anomaly detection algorithm can be used for scoring individual anomalies, both supervised and unsupervised approaches. The applicability of the proposed approach is shown by applying it to an artificial data set and to two industrial data sets — detecting anomalously moving cranes (model-based detection) and anomalous fuel consumption (neighbour-based detection).

Place, publisher, year, edition, pages
2015, 7.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:ri:diva-24430OAI: oai:DiVA.org:ri-24430DiVA: diva2:1043511
Conference
Proceedings of the Twenty-Eighth International Florida Artificial Intelligence Research Society Conference
Projects
STREAM
Available from: 2016-10-31 Created: 2016-10-31Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
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
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