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An Introspective Comparison of Random Forest-Based Classifiers for the Analysis of Cluster-Correlated Data by Way of RF++
Many mass spectrometry-based studies, as well as other biological experiments produce cluster-correlated data. Failure to account for correlation among observations may result in a classification algorithm overfitting the training data and producing overoptimistic estimated error rates and may make...
| Autores principales: | , , , , |
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| Formato: | Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2009
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2739274/ https://www.ncbi.nlm.nih.gov/pubmed/19763254 http://dx.doi.org/10.1371/journal.pone.0007087 |