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SMOTE for high-dimensional class-imbalanced data

BACKGROUND: Classification using class-imbalanced data is biased in favor of the majority class. The bias is even larger for high-dimensional data, where the number of variables greatly exceeds the number of samples. The problem can be attenuated by undersampling or oversampling, which produce class...

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Detalhes bibliográficos
Main Authors: Blagus, Rok, Lusa, Lara
Formato: Artigo
Idioma:Inglês
Publicado em: BioMed Central 2013
Assuntos:
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC3648438/
https://ncbi.nlm.nih.gov/pubmed/23522326
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/1471-2105-14-106
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