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Anomaly Detection Through a Bayesian Support Vector Machine
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DOI：10.1109/TR.2010.2048740.png)
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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.
Keyword:
Anomaly detection
Bayesian linear models
Bayesian posterior class probabilities
kernel density estimation
one-class classifier
support vector machine
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