Synthetic minority oversampling of vital statistics data with generative adversarial networks

OBJECTIVE: Minority oversampling is a standard approach used for adjusting the ratio between the classes on imbalanced data. However, established methods often provide modest improvements in classification performance when applied to data with extremely imbalanced class distribution and to mixed-typ...

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Detalles Bibliográficos
Autores principales: Koivu, Aki, Sairanen, Mikko, Airola, Antti, Pahikkala, Tapio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Research and Applications
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7750982/
https://www.ncbi.nlm.nih.gov/pubmed/32885818
http://dx.doi.org/10.1093/jamia/ocaa127

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7750982/
https://www.ncbi.nlm.nih.gov/pubmed/32885818
http://dx.doi.org/10.1093/jamia/ocaa127

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