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Recommendations to Handle Health-related Small Imbalanced Data in Machine Learning

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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.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.identifier.doi10.18420/muc2020-ws111-333
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/33508
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/Workshop Paper
gi.citation.publisherPlaceBonn
gi.conference.date6.-9. September 2020
gi.conference.locationMagdeburg
gi.conference.sessiontitleMCI-WS02: UCAI 2020: Workshop on User-Centered Artificial Intelligence
gi.document.qualitydigidoc

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