A consistency-based model selection for one-class classification

David M.J. Tax; Klaus Robert Müller

doi:10.1109/icpr.2004.1334542

A consistency-based model selection for one-class classification

David M.J. Tax, Klaus Robert Müller

Research output: Contribution to journal › Conference article › peer-review

54 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	363-366
Number of pages	4
Journal	Proceedings - International Conference on Pattern Recognition
Volume	3
DOIs	https://doi.org/10.1109/icpr.2004.1334542
Publication status	Published - 2004
Externally published	Yes
Event	Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom Duration: 2004 Aug 23 → 2004 Aug 26

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

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10.1109/icpr.2004.1334542

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ER -

A consistency-based model selection for one-class classification

Abstract

ASJC Scopus subject areas

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