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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...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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BioMed Central
2013
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3648438/ https://www.ncbi.nlm.nih.gov/pubmed/23522326 http://dx.doi.org/10.1186/1471-2105-14-106 |