Empirical characterization of random forest variable importance measures

Microarray studies yield data sets consisting of a large number of candidate predictors (genes) on a small number of observations (samples). When interest lies in predicting phenotypic class using gene expression data, often the goals are both to produce an accurate classifier and to uncover the pre...

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Bibliographic Details
Published in:	Computational statistics & data analysis Vol. 52; no. 4; pp. 2249 - 2260
Main Authors:	Archer, Kellie J., Kimes, Ryan V.
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.01.2008 Elsevier Science Elsevier
Series:	Computational Statistics & Data Analysis
Subjects:	Bootstrap aggregating Classification tree Exact sciences and technology General topics Mathematics Multivariate analysis Numerical analysis Numerical analysis. Scientific computation Numerical methods in probability and statistics Probability and statistics Probability theory and stochastic processes Random forest Sciences and techniques of general use Statistics Stochastic processes Variable importance Random forest Variable importance Classification tree Bootstrap aggregating Correlation Forests Random measure Multivariate analysis Covariate Stochastic process Statistical simulation Microarray Learning Characterization Law of large numbers Random variable Discriminant analysis Data analysis Prior distribution Statistical association Statistical estimation Neural network Statistical computation Correlation analysis Small sample
ISSN:	0167-9473, 1872-7352
Online Access:	Get full text
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