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A simple plug-in bagging ensemble based on threshold-moving for classifying binary and multiclass imbalanced data
Class imbalance presents a major hurdle in the application of classification methods. A commonly taken approach is to learn ensembles of classifiers using rebalanced data. Examples include bootstrap averaging (bagging) combined with either undersampling or oversampling of the minority class examples...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier Science Publishers
2018
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5750819/ https://www.ncbi.nlm.nih.gov/pubmed/29398782 http://dx.doi.org/10.1016/j.neucom.2017.08.035 |