A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data

BACKGROUND: Regularized regression methods such as principal component or partial least squares regression perform well in learning tasks on high dimensional spectral data, but cannot explicitly eliminate irrelevant features. The random forest classifier with its associated Gini feature importance,...

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Detalles Bibliográficos
Autores principales: Menze, Bjoern H, Kelm, B Michael, Masuch, Ralf, Himmelreich, Uwe, Bachert, Peter, Petrich, Wolfgang, Hamprecht, Fred A
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2724423/
https://www.ncbi.nlm.nih.gov/pubmed/19591666
http://dx.doi.org/10.1186/1471-2105-10-213

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2724423/
https://www.ncbi.nlm.nih.gov/pubmed/19591666
http://dx.doi.org/10.1186/1471-2105-10-213

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