Using Under-Trained Deep Ensembles to Learn under Extreme Label Noise: A Case Study for Sleep Apnea Detection

10.1109/access.2021.3067455

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Bibliographic Details
Main Authors:	Nikolaidis, Konstantinos, Plagemann, Thomas, Kristiansen, Stein, Goebel, Vera, Kankanhalli, Mohan
Other Authors:	DEPARTMENT OF INFORMATION SYSTEMS AND ANALYTICS
Format:	Article
Published:	Institute of Electrical and Electronics Engineers Inc. 2022
Subjects:	Biomedical informatics label noise machine learning sleep apnea supervised learning
Online Access:	https://scholarbank.nus.edu.sg/handle/10635/232870
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Institution:	National University of Singapore

id	sg-nus-scholar.10635-232870
record_format	dspace
spelling	sg-nus-scholar.10635-2328702024-11-09T05:31:38Z Using Under-Trained Deep Ensembles to Learn under Extreme Label Noise: A Case Study for Sleep Apnea Detection Nikolaidis, Konstantinos Plagemann, Thomas Kristiansen, Stein Goebel, Vera Kankanhalli, Mohan DEPARTMENT OF INFORMATION SYSTEMS AND ANALYTICS Biomedical informatics label noise machine learning sleep apnea supervised learning 10.1109/access.2021.3067455 IEEE Access 9 45919-45934 2022-10-13T01:16:03Z 2022-10-13T01:16:03Z 2021-01-01 Article Nikolaidis, Konstantinos, Plagemann, Thomas, Kristiansen, Stein, Goebel, Vera, Kankanhalli, Mohan (2021-01-01). Using Under-Trained Deep Ensembles to Learn under Extreme Label Noise: A Case Study for Sleep Apnea Detection. IEEE Access 9 : 45919-45934. ScholarBank@NUS Repository. https://doi.org/10.1109/access.2021.3067455 2169-3536 https://scholarbank.nus.edu.sg/handle/10635/232870 Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ Institute of Electrical and Electronics Engineers Inc. Scopus OA2021
institution	National University of Singapore
building	NUS Library
continent	Asia
country	Singapore Singapore
content_provider	NUS Library
collection	ScholarBank@NUS
topic	Biomedical informatics label noise machine learning sleep apnea supervised learning
spellingShingle	Biomedical informatics label noise machine learning sleep apnea supervised learning Nikolaidis, Konstantinos Plagemann, Thomas Kristiansen, Stein Goebel, Vera Kankanhalli, Mohan Using Under-Trained Deep Ensembles to Learn under Extreme Label Noise: A Case Study for Sleep Apnea Detection
description	10.1109/access.2021.3067455
author2	DEPARTMENT OF INFORMATION SYSTEMS AND ANALYTICS
author_facet	DEPARTMENT OF INFORMATION SYSTEMS AND ANALYTICS Nikolaidis, Konstantinos Plagemann, Thomas Kristiansen, Stein Goebel, Vera Kankanhalli, Mohan
format	Article
author	Nikolaidis, Konstantinos Plagemann, Thomas Kristiansen, Stein Goebel, Vera Kankanhalli, Mohan
author_sort	Nikolaidis, Konstantinos
title	Using Under-Trained Deep Ensembles to Learn under Extreme Label Noise: A Case Study for Sleep Apnea Detection
title_short	Using Under-Trained Deep Ensembles to Learn under Extreme Label Noise: A Case Study for Sleep Apnea Detection
title_full	Using Under-Trained Deep Ensembles to Learn under Extreme Label Noise: A Case Study for Sleep Apnea Detection
title_fullStr	Using Under-Trained Deep Ensembles to Learn under Extreme Label Noise: A Case Study for Sleep Apnea Detection
title_full_unstemmed	Using Under-Trained Deep Ensembles to Learn under Extreme Label Noise: A Case Study for Sleep Apnea Detection
title_sort	using under-trained deep ensembles to learn under extreme label noise: a case study for sleep apnea detection
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2022
url	https://scholarbank.nus.edu.sg/handle/10635/232870
_version_	1821205304506843136

Using Under-Trained Deep Ensembles to Learn under Extreme Label Noise: A Case Study for Sleep Apnea Detection

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