Anomaly detection through a bayesian support vector machine

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

101 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Article number5475445
Pages (from-to)277-286
Journal / PublicationIEEE Transactions on Reliability
Volume59
Issue number2
Publication statusPublished - Jun 2010

Abstract

This paper investigates the use of a one-class support vector machine algorithm to detect the onset of system anomalies, and trend output classification probabilities, as a way to monitor the health of a system. In the absence of unhealthy (negative class) information, a marginal kernel density estimate of the healthy (positive class) distribution is used to construct an estimate of the negative class. The output of the one-class support vector classifier is calibrated to posterior probabilities by fitting a logistic distribution to the support vector predictor model in an effort to manage false alarms. © 2006 IEEE.

Research Area(s)

  • Anomaly detection, Bayesian linear models, Bayesian posterior class probabilities, Kernel density estimation, One-class classifier, Support vector machine

Citation Format(s)

Anomaly detection through a bayesian support vector machine. / Sotiris, Vasilis A.; Tse, Peter W.; Pecht, Michael G.
In: IEEE Transactions on Reliability, Vol. 59, No. 2, 5475445, 06.2010, p. 277-286.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review