A consistency-based model selection for one-class classification

David M.J. Tax, Klaus Robert Müller

Research output: Contribution to journalConference articlepeer-review

54 Citations (Scopus)


Model selection in unsupervised learning is a hard problem. In this paper a simple selection criterion for hyperparameters in one-class classifiers (OCCs) is proposed. It makes use of the particular structure of the one-class problem. The mean idea is that the complexity of the classifier is increased until the classifier becomes inconsistent on the target class. This defines the most complex classifier which can still reliably be trained on the data. Experiments indicated the usefulness of the approach.

Original languageEnglish
Pages (from-to)363-366
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Publication statusPublished - 2004
Externally publishedYes
EventProceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom
Duration: 2004 Aug 232004 Aug 26

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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