Combining one-class classifiers via meta learning

Eitan Menahem, Lior Rokach, Yuval Elovici

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations

Abstract

Selecting the best classifier among the available ones is a difficult task, especially when only instances of one class exist. In this work we examine the notion of combining one-class classifiers as an alternative for selecting the best classifier. In particular, we propose two one-class classification performance measures to weigh classifiers and show that a simple ensemble that implements these measures can outperform the most popular one-class ensembles. Furthermore, we propose a new one-class ensemble scheme, TUPSO, which uses meta-learning to combine one-class classifiers. Our experiments demonstrate the superiority of TUPSO over all other tested ensembles and show that the TUPSO performance is statistically indistinguishable from that of the hypothetical best classifier.

Original languageEnglish
Title of host publicationCIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management
Pages2435-2440
Number of pages6
DOIs
StatePublished - 11 Dec 2013
Event22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 - San Francisco, CA, United States
Duration: 27 Oct 20131 Nov 2013

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
Country/TerritoryUnited States
CitySan Francisco, CA
Period27/10/131/11/13

Keywords

  • Ensemble of classifiers
  • Meta learning
  • One-class ensemble

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