Logo des Repositoriums

Auflistung nach Schlagwort "over-fitting"

1 - 2 von 2
  • Zeitschriftenartikel
    How to Handle Health-Related Small Imbalanced Data in Machine Learning?
    (i-com: Vol. 19, No. 3, 2021) Rauschenberger, Maria; Baeza-Yates, Ricardo
    When discussing interpretable machine learning results, researchers need to compare them and check for reliability, especially for health-related data. The reason is the negative impact of wrong results on a person, such as in wrong prediction of cancer, incorrect assessment of the COVID-19 pandemic situation, or missing early screening of dyslexia. Often only small data exists for these complex interdisciplinary research projects. Hence, it is essential that this type of research understands different methodologies and mindsets such as the Design Science Methodology, Human-Centered Design or Data Science approaches to ensure interpretable and reliable results. Therefore, we present various recommendations and design considerations for experiments that help to avoid over-fitting and biased interpretation of results when having small imbalanced data related to health. We also present two very different use cases: early screening of dyslexia and event prediction in multiple sclerosis.
  • Workshopbeitrag
    Recommendations to Handle Health-related Small Imbalanced Data in Machine Learning
    (Mensch und Computer 2020 - Workshopband, 2020) Rauschenberger, Maria; Baeza-Yates, Ricardo
    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.