Ensemble algorithms for unsupervised anomaly detection

Zhiruo Zhao, Kishan G. Mehrotra, Chilukuri K Mohan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Many anomaly detection algorithms have been proposed in recent years, including density-based and rank-based algorithms. In this paper, we propose ensemble methods to improve the performance of these individual algorithms. We evaluate approaches that use score and rank aggregation for these algorithms. We also consider sequential methods in which one detection method is followed by the other. We use several datasets to evaluate the performance of the proposed ensemble methods. Our results show that sequential methods significantly improve the ability to detect anomalous data points.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages514-525
Number of pages12
Volume9101
ISBN (Print)9783319190655
DOIs
StatePublished - 2015
Event28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015 - Seoul, Korea, Republic of
Duration: Jun 10 2015Jun 12 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9101
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015
CountryKorea, Republic of
CitySeoul
Period6/10/156/12/15

Keywords

  • Anomaly detection
  • Density-based anomaly detection
  • Ensemble method
  • Rank-based anomaly detection

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Zhao, Z., Mehrotra, K. G., & Mohan, C. K. (2015). Ensemble algorithms for unsupervised anomaly detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9101, pp. 514-525). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9101). Springer Verlag. https://doi.org/10.1007/978-3-319-19066-2_50