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Unbiased Feature Selection in Learning Random Forests for High-Dimensional Data
Random forests (RFs) have been widely used as a powerful classification method. However, with the randomization in both bagging samples and feature selection, the trees in the forest tend to select uninformative features for node splitting. This makes RFs have poor accuracy when working with high-di...
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| Publicado en: | ScientificWorldJournal |
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| Autores principales: | , , |
| Formato: | Artigo |
| Lenguaje: | Inglês |
| Publicado: |
Hindawi Publishing Corporation
2015
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| Materias: | |
| Acceso en línea: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4387916/ https://ncbi.nlm.nih.gov/pubmed/25879059 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1155/2015/471371 |
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