Data perturbation for outlier detection ensembles

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Outlier detection and ensemble learning are well established research directions in data mining yet the application of ensemble techniques to outlier detection has been rarely studied. Building an ensemble requires learning of diverse models and combining these diverse models in an appropriate way. We propose data perturbation as a new technique to induce diversity in individual outlier detectors as well as a rank accumulation method for the combination of the individual outlier rankings in order to construct an outlier detection ensemble. In an extensive evaluation, we study the impact, potential, and shortcomings of this new approach for outlier detection ensembles. We show that this ensemble can significantly improve over weak performing base methods.

Original languageEnglish
Title of host publicationProceedings of the 26th International Conference on Scientific and Statistical Database Management
Number of pages12
PublisherAssociation for Computing Machinery
Publication date2014
Article number13
ISBN (Print)978-1-4503-2722-0
DOIs
Publication statusPublished - 2014
Externally publishedYes
EventInternational Conference on Scientific and Statistical Database Management - Aalborg, Denmark
Duration: 30. Jun 20142. Jul 2014

Conference

ConferenceInternational Conference on Scientific and Statistical Database Management
Country/TerritoryDenmark
CityAalborg
Period30/06/201402/07/2014
SponsorDanish Otto Monsted Foundation, TARGIT

Keywords

  • Ensemble
  • Outlier detection

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