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Proceedings Paper

Model selection for anomaly detection
Author(s): E. Burnaev; P. Erofeev; D. Smolyakov
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Paper Abstract

Anomaly detection based on one-class classification algorithms is broadly used in many applied domains like image processing (e.g. detection of whether a patient is “cancerous” or “healthy” from mammography image), network intrusion detection, etc. Performance of an anomaly detection algorithm crucially depends on a kernel, used to measure similarity in a feature space. The standard approaches (e.g. cross-validation) for kernel selection, used in two-class classification problems, can not be used directly due to the specific nature of a data (absence of a second, abnormal, class data). In this paper we generalize several kernel selection methods from binary-class case to the case of one-class classification and perform extensive comparison of these approaches using both synthetic and real-world data.

Paper Details

Date Published: 8 December 2015
PDF: 6 pages
Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 987525 (8 December 2015); doi: 10.1117/12.2228794
Show Author Affiliations
E. Burnaev, Institute for Information Transmission Problems (Russian Federation)
P. Erofeev, Institute for Information Transmission Problems (Russian Federation)
D. Smolyakov, Institute for Information Transmission Problems (Russian Federation)


Published in SPIE Proceedings Vol. 9875:
Eighth International Conference on Machine Vision (ICMV 2015)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev, Editor(s)

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