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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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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. |
format | Online Article Text |
id | pubmed-5948704 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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