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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...
Αποθηκεύτηκε σε:
| Κύριοι συγγραφείς: | , |
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| Μορφή: | Artigo |
| Γλώσσα: | Inglês |
| Έκδοση: |
BioMed Central
2013
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| Θέματα: | |
| Διαθέσιμο Online: | 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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