TY - GEN
T1 - Mixture of designer experts for multi-regime detection in streaming data
AU - Kriminger, Evan
AU - Príncipe, José
AU - Lakshminarayan, Choudur
PY - 2012
Y1 - 2012
N2 - Real-time streaming data takes on distinct visible patterns, known as regimes, as a result of changing external influences. Regimes corresponding to hazardous states, such as turbulent flow in oil pipelines or patients experiencing heart arrhythmias, must be identified quickly and accurately by on-line detection algorithms. In this paper, we propose a modification to the mixture of experts framework, which is traditionally used to model piecewise stationary time series. Our proposed modification allows experts to produce features specific to their designated regimes, rather than being limited to prediction error. This approach provides the flexibility to update the mixture modularly as new regimes emerge without the burden of retraining the entire mixture, as is typical in traditional classifiers. Our approach is tested on flow rate data from an oil and gas application, as well as detecting heart arrhythmias from electrocardiogram (ECG) signals. It outperforms traditional classification approaches both in terms of error rate and detector delay.
AB - Real-time streaming data takes on distinct visible patterns, known as regimes, as a result of changing external influences. Regimes corresponding to hazardous states, such as turbulent flow in oil pipelines or patients experiencing heart arrhythmias, must be identified quickly and accurately by on-line detection algorithms. In this paper, we propose a modification to the mixture of experts framework, which is traditionally used to model piecewise stationary time series. Our proposed modification allows experts to produce features specific to their designated regimes, rather than being limited to prediction error. This approach provides the flexibility to update the mixture modularly as new regimes emerge without the burden of retraining the entire mixture, as is typical in traditional classifiers. Our approach is tested on flow rate data from an oil and gas application, as well as detecting heart arrhythmias from electrocardiogram (ECG) signals. It outperforms traditional classification approaches both in terms of error rate and detector delay.
KW - Detection
KW - mixture of experts
KW - streaming data
UR - http://www.scopus.com/inward/record.url?scp=84869753432&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84869753432&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84869753432
SN - 9781467310680
T3 - European Signal Processing Conference
SP - 410
EP - 414
BT - Proceedings of the 20th European Signal Processing Conference, EUSIPCO 2012
T2 - 20th European Signal Processing Conference, EUSIPCO 2012
Y2 - 27 August 2012 through 31 August 2012
ER -