8 December 2015 Model selection for anomaly detection
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Proceedings Volume 9875, Eighth International Conference on Machine Vision (ICMV 2015); 987525 (2015) https://doi.org/10.1117/12.2228794
Event: Eighth International Conference on Machine Vision, 2015, Barcelona, Spain
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.
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E. Burnaev, P. Erofeev, D. Smolyakov, "Model selection for anomaly detection", Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 987525 (8 December 2015); doi: 10.1117/12.2228794; https://doi.org/10.1117/12.2228794

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