Learning to Fairly Classify the Quality of Wireless Links

dc.contributor.author	Cerar, Gregor
dc.contributor.author	Yetgin, Halil
dc.contributor.author	Mohorcic, Mihael
dc.contributor.author	Fortuna, Carolina
dc.date.accessioned	16/12/21 12:06
dc.date.available	16/12/21 12:06
dc.date.issued	2021
dc.identifier.isbn	978-3-903176-35-5
dc.identifier.uri	http://dspace.beu.edu.tr:8080/xmlui/handle/20.500.12643/9888
dc.identifier.uri	https://doi.org/10.23919/WONS51326.2021.9415540
dc.description.abstract	Machine learning (ML) has been used to develop increasingly accurate link quality estimators for wireless networks. However, more in depth questions regarding the most suitable class of models, most suitable metrics and model performance on imbalanced dat
dc.description.sponsorship	Slovenian Research AgencySlovenian Research Agency - Slovenia [P2-0016, J2-9232]; EC H2020 NRG-5 Project [762013]
dc.language.iso	English
dc.publisher	Ieee
dc.relation.ispartof	16th Annual Conference on Wireless On-demand Network Systems and Services (WONS)
dc.rights	Green Submitted
dc.source	2021 16Th Annual Conference On Wıreless On-Demand Network Systems And Servıces Conference (Wons)
dc.title	Learning to Fairly Classify the Quality of Wireless Links
dc.type	Proceedings Paper
dc.identifier.startpage	79
dc.identifier.endpage	86
dc.identifier.doi	10.23919/WONS51326.2021.9415540
dc.identifier.wos	WOS:000678993200013

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