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dc.contributor.authorRauschenberger, Maria
dc.contributor.authorBaeza-Yates, Ricardo
dc.contributor.editorHansen, Christian
dc.contributor.editorNürnberger, Andreas
dc.contributor.editorPreim, Bernhard
dc.date.accessioned2020-08-18T15:19:48Z
dc.date.available2020-08-18T15:19:48Z
dc.date.issued2020
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/33508
dc.description.abstractWhen discussing interpretable machine learning results, researchers need to compare results and reflect on reliable results, especially for health-related data. The reason is the negative impact of wrong results on a person, such as in missing early screening of dyslexia or wrong prediction of cancer. We present nine criteria that help avoiding over-fitting and biased interpretation of results when having small imbalanced data related to health. We present a use case of early screening of dyslexia with an imbalanced data set using machine learning classification to explain design decisions and discuss issues for further research.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofMensch und Computer 2020 - Workshopband
dc.relation.ispartofseriesMensch und Computer
dc.subjectMachine Learning
dc.subjectHuman-Centered Design
dc.subjectHCD
dc.subjectinteractive systems
dc.subjecthealth
dc.subjectsmall data
dc.subjectimbalanced data
dc.subjectover-fitting
dc.subjectvariances
dc.subjectinterpretable results
dc.subjectguidelines.
dc.titleRecommendations to Handle Health-related Small Imbalanced Data in Machine Learningen
dc.typeText/Conference Poster
dc.pubPlaceBonn
mci.document.qualitydigidoc
mci.conference.sessiontitleMCI-WS02: UCAI 2020: Workshop on User-Centered Artificial Intelligence
mci.conference.locationMagdeburg
mci.conference.date6.-9. September 2020
dc.identifier.doi10.18420/muc2020-ws111-333


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