TY - JOUR
T1 - Anomaly detection through a bayesian support vector machine
AU - Sotiris, Vasilis A.
AU - Tse, Peter W.
AU - Pecht, Michael G.
PY - 2010/6
Y1 - 2010/6
N2 - 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.
AB - 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.
KW - Anomaly detection
KW - Bayesian linear models
KW - Bayesian posterior class probabilities
KW - Kernel density estimation
KW - One-class classifier
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=77953098563&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-77953098563&origin=recordpage
U2 - 10.1109/TR.2010.2048740
DO - 10.1109/TR.2010.2048740
M3 - RGC 21 - Publication in refereed journal
VL - 59
SP - 277
EP - 286
JO - IEEE Transactions on Reliability
JF - IEEE Transactions on Reliability
SN - 0018-9529
IS - 2
M1 - 5475445
ER -