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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Detalhes bibliográficos
Principais autores: Menze, Bjoern H, Kelm, B Michael, Masuch, Ralf, Himmelreich, Uwe, Bachert, Peter, Petrich, Wolfgang, Hamprecht, Fred A
Formato: Artigo
Idioma:Inglês
Publicado em: BioMed Central 2009
Assuntos:
Research Article
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC2724423/
https://ncbi.nlm.nih.gov/pubmed/19591666
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/1471-2105-10-213
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