Implications of resampling data to address the class imbalance problem (IRCIP): an evaluation of impact on performance between classification algorithms in medical data

OBJECTIVE: When correcting for the “class imbalance” problem in medical data, the effects of resampling applied on classifier algorithms remain unclear. We examined the effect on performance over several combinations of classifiers and resampling ratios. MATERIALS AND METHODS: Multiple classificatio...

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Detalles Bibliográficos
Autores principales: Welvaars, Koen, Oosterhoff, Jacobien H F, van den Bekerom, Michel P J, Doornberg, Job N, van Haarst, Ernst P
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Research and Applications
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10232287/
https://www.ncbi.nlm.nih.gov/pubmed/37266187
http://dx.doi.org/10.1093/jamiaopen/ooad033

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10232287/
https://www.ncbi.nlm.nih.gov/pubmed/37266187
http://dx.doi.org/10.1093/jamiaopen/ooad033

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