Anomaly Detection Through a Bayesian Support Vector Machine
Vasilis A. Sotiris, Peter Wai-Tat Tse, MICHAEL G. PECHT · IEEE Transactions on Reliability · 2010
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.