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Optimize the Coverage Probability of Prediction Interval for Anomaly Detection of Sensor-Based Monitoring Series

Effective anomaly detection of sensing data is essential for identifying potential system failures. Because they require no prior knowledge or accumulated labels, and provide uncertainty presentation, the probability prediction methods (e.g., Gaussian process regression (GPR) and relevance vector ma...

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Detalles Bibliográficos
Autores principales: Pang, Jingyue, Liu, Datong, Peng, Yu, Peng, Xiyuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948704/
https://www.ncbi.nlm.nih.gov/pubmed/29587372
http://dx.doi.org/10.3390/s18040967
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author Pang, Jingyue
Liu, Datong
Peng, Yu
Peng, Xiyuan
author_facet Pang, Jingyue
Liu, Datong
Peng, Yu
Peng, Xiyuan
author_sort Pang, Jingyue
collection PubMed
description Effective anomaly detection of sensing data is essential for identifying potential system failures. Because they require no prior knowledge or accumulated labels, and provide uncertainty presentation, the probability prediction methods (e.g., Gaussian process regression (GPR) and relevance vector machine (RVM)) are especially adaptable to perform anomaly detection for sensing series. Generally, one key parameter of prediction models is coverage probability (CP), which controls the judging threshold of the testing sample and is generally set to a default value (e.g., 90% or 95%). There are few criteria to determine the optimal CP for anomaly detection. Therefore, this paper designs a graphic indicator of the receiver operating characteristic curve of prediction interval (ROC-PI) based on the definition of the ROC curve which can depict the trade-off between the PI width and PI coverage probability across a series of cut-off points. Furthermore, the Youden index is modified to assess the performance of different CPs, by the minimization of which the optimal CP is derived by the simulated annealing (SA) algorithm. Experiments conducted on two simulation datasets demonstrate the validity of the proposed method. Especially, an actual case study on sensing series from an on-orbit satellite illustrates its significant performance in practical application.
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spelling pubmed-59487042018-05-17 Optimize the Coverage Probability of Prediction Interval for Anomaly Detection of Sensor-Based Monitoring Series Pang, Jingyue Liu, Datong Peng, Yu Peng, Xiyuan Sensors (Basel) Article Effective anomaly detection of sensing data is essential for identifying potential system failures. Because they require no prior knowledge or accumulated labels, and provide uncertainty presentation, the probability prediction methods (e.g., Gaussian process regression (GPR) and relevance vector machine (RVM)) are especially adaptable to perform anomaly detection for sensing series. Generally, one key parameter of prediction models is coverage probability (CP), which controls the judging threshold of the testing sample and is generally set to a default value (e.g., 90% or 95%). There are few criteria to determine the optimal CP for anomaly detection. Therefore, this paper designs a graphic indicator of the receiver operating characteristic curve of prediction interval (ROC-PI) based on the definition of the ROC curve which can depict the trade-off between the PI width and PI coverage probability across a series of cut-off points. Furthermore, the Youden index is modified to assess the performance of different CPs, by the minimization of which the optimal CP is derived by the simulated annealing (SA) algorithm. Experiments conducted on two simulation datasets demonstrate the validity of the proposed method. Especially, an actual case study on sensing series from an on-orbit satellite illustrates its significant performance in practical application. MDPI 2018-03-24 /pmc/articles/PMC5948704/ /pubmed/29587372 http://dx.doi.org/10.3390/s18040967 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pang, Jingyue
Liu, Datong
Peng, Yu
Peng, Xiyuan
Optimize the Coverage Probability of Prediction Interval for Anomaly Detection of Sensor-Based Monitoring Series
title Optimize the Coverage Probability of Prediction Interval for Anomaly Detection of Sensor-Based Monitoring Series
title_full Optimize the Coverage Probability of Prediction Interval for Anomaly Detection of Sensor-Based Monitoring Series
title_fullStr Optimize the Coverage Probability of Prediction Interval for Anomaly Detection of Sensor-Based Monitoring Series
title_full_unstemmed Optimize the Coverage Probability of Prediction Interval for Anomaly Detection of Sensor-Based Monitoring Series
title_short Optimize the Coverage Probability of Prediction Interval for Anomaly Detection of Sensor-Based Monitoring Series
title_sort optimize the coverage probability of prediction interval for anomaly detection of sensor-based monitoring series
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948704/
https://www.ncbi.nlm.nih.gov/pubmed/29587372
http://dx.doi.org/10.3390/s18040967
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