Model selection for anomaly detection

E. Burnaev, P. Erofeev, D. Smolyakov

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

21 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationEighth International Conference on Machine Vision, ICMV 2015
EditorsAntanas Verikas, Petia Radeva, Dmitry Nikolaev
ISBN (Electronic)9781510601161
Publication statusPublished - 2015
Externally publishedYes
Event8th International Conference on Machine Vision, ICMV 2015 - Barcelona, Spain
Duration: 19 Nov 201521 Nov 2015

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Conference8th International Conference on Machine Vision, ICMV 2015


  • anomaly detection
  • empirical risk
  • kernel width
  • model selection
  • one-class classification
  • SVDD


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