Logo des Repositoriums
 
Workshopbeitrag

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

Vorschaubild nicht verfügbar

Volltext URI

Dokumententyp

Text/Workshop Paper

Zusatzinformation

Datum

2020

Zeitschriftentitel

ISSN der Zeitschrift

Bandtitel

Verlag

Gesellschaft für Informatik e.V.

Zusammenfassung

When 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.

Beschreibung

Rauschenberger, Maria; Baeza-Yates, Ricardo (2020): Recommendations to Handle Health-related Small Imbalanced Data in Machine Learning. Mensch und Computer 2020 - Workshopband. DOI: 10.18420/muc2020-ws111-333. Bonn: Gesellschaft für Informatik e.V.. MCI-WS02: UCAI 2020: Workshop on User-Centered Artificial Intelligence. Magdeburg. 6.-9. September 2020

Zitierform

Tags