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dc.contributor.authorDettling, Marcel-
dc.contributor.authorFrey, Martin-
dc.contributor.authorWalser, Manuel-
dc.contributor.authorHaas, Patrick-
dc.date.accessioned2021-08-19T12:34:40Z-
dc.date.available2021-08-19T12:34:40Z-
dc.date.issued2021-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/22977-
dc.description.abstractThis contribution revolves around classifying football player actions with 1-dimensional convolutional neural networks (CNNs) based on 6-channel inertial motion unit (IMU) data arising from tracking devices worn by the players. Our training and test data consist of eight games, where humans labelled ball actions by inspecting video records. Unfortunately, these labels are far from perfect due to various reasons (e.g., sloppiness, not all players respectively ball actions visible in the record, ambiguity what a ball action is, etc.). Such mislabelled data provide challenges on several levels. First, performance evaluation with poorly annotated data can be strongly misleading, indicating inferior performance than what is truly achieved. Second, the question is what amount of mislabelled data deep artificial neural networks can tolerate before they break down. We try to shed some light on the magnitude of these effects by simulation studies on the football data, as well as some standard machine learning datasets such as MNIST (numbers) and Fashion-MNIST (clothes). Third, we present some efficient strategies to overcome the issue with imperfect labels and aim to provide some guidelines how to efficiently invest effort in labelling data.de_CH
dc.language.isoende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectDeep learningde_CH
dc.subjectMislabellingde_CH
dc.subjectSequential data analysisde_CH
dc.subjectTime series classificationde_CH
dc.subjectSports analyticsde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleImpact of mislabelling on deep learning methods and strategies for improvementde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
zhaw.conference.details63rd ISI World Statistics Congress, virtual, 11-16 July 2021de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Abstract)de_CH
zhaw.title.proceedingsConference Proceedings of the 63rd ISI World Statistics Congressde_CH
zhaw.webfeedDatalabde_CH
zhaw.funding.zhawEntwicklung von Algorithmen zur Analyse von Fussballspielern und Spielsituationen anhand von Bewegungsdatende_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Dettling, M., Frey, M., Walser, M., & Haas, P. (2021). Impact of mislabelling on deep learning methods and strategies for improvement. Conference Proceedings of the 63rd ISI World Statistics Congress.
Dettling, M. et al. (2021) ‘Impact of mislabelling on deep learning methods and strategies for improvement’, in Conference Proceedings of the 63rd ISI World Statistics Congress.
M. Dettling, M. Frey, M. Walser, and P. Haas, “Impact of mislabelling on deep learning methods and strategies for improvement,” in Conference Proceedings of the 63rd ISI World Statistics Congress, 2021.
DETTLING, Marcel, Martin FREY, Manuel WALSER und Patrick HAAS, 2021. Impact of mislabelling on deep learning methods and strategies for improvement. In: Conference Proceedings of the 63rd ISI World Statistics Congress. Conference paper. 2021
Dettling, Marcel, Martin Frey, Manuel Walser, and Patrick Haas. 2021. “Impact of Mislabelling on Deep Learning Methods and Strategies for Improvement.” Conference paper. In Conference Proceedings of the 63rd ISI World Statistics Congress.
Dettling, Marcel, et al. “Impact of Mislabelling on Deep Learning Methods and Strategies for Improvement.” Conference Proceedings of the 63rd ISI World Statistics Congress, 2021.


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