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...

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Detalles Bibliográficos
Autores principales: Karpievitch, Yuliya V., Hill, Elizabeth G., Leclerc, Anthony P., Dabney, Alan R., Almeida, Jonas S.
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2009
Materias:
Research Article
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

Internet

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

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