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DC poleHodnotaJazyk
dc.contributor.authorSperling, Leo
dc.contributor.authorLämmer, Simon
dc.contributor.authorHagen, Hans
dc.contributor.authorScheuermann, Gerik
dc.contributor.authorGillmann, Christina
dc.contributor.editorSkala, Václav
dc.date.accessioned2022-08-29T10:34:30Z
dc.date.available2022-08-29T10:34:30Z
dc.date.issued2022
dc.identifier.citationJournal of WSCG. 2022, vol. 30, no. 1-2, p. 63-71.en
dc.identifier.issn1213-6972 (print)
dc.identifier.issn1213-6964 (on-line)
dc.identifier.urihttp://hdl.handle.net/11025/49395
dc.format9 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjecthodnotící opatřenícs
dc.subjectnejistota-uvědoměnícs
dc.subjectstrojové učenícs
dc.titleUncertainty-aware Evaluation of Machine Learning Performance in binary Classification Tasksen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedMachine learning has become a standard tool in computer vision. Nowadays, neural networks are one of the most prominent representatives in this class of algorithms that usually require training and evaluation to work as desired. There exist a variety of evaluation metrics to determine the quality of a trained neural network, which are usually threshold dependent. This results in massive changes in the resulting evaluation when the threshold is changed slightly. Further, measurements of uncertainty such as resulting from Bayesian approaches, are not considered in this analysis. In this paper, we present evaluation metrics for machine learning approaches that are able to attach a probability distribution to the utilized threshold and include uncertainty measures. We demonstrate the applicability of our approach by applying the defined metrics to a real-world example where a Bayesian neural network has been used to predict stroke lesions.en
dc.subject.translatedevaluation measuresen
dc.subject.translateduncertainty-awarenessen
dc.subject.translatedmachine learningen
dc.identifier.doihttps://www.doi.org/10.24132/JWSCG.2022.8
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Volume 30, Number 1-2 (2021)

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  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Časopisy ZČU / UWB Journals
  4. Journal of WSCG
  5. Volume 30 (2022)
  6. Volume 30, Number 1-2 (2021)