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Using random forest for reliable classification and cost-sensitive learning for medical diagnosis
BACKGROUND: Most machine-learning classifiers output label predictions for new instances without indicating how reliable the predictions are. The applicability of these classifiers is limited in critical domains where incorrect predictions have serious consequences, like medical diagnosis. Further,...
| Autores principales: | , , , , |
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| Formato: | Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2009
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2648734/ https://www.ncbi.nlm.nih.gov/pubmed/19208122 http://dx.doi.org/10.1186/1471-2105-10-S1-S22 |