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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Summary: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 predictive structure of the problem. Most machine learning methods, such as k -nearest neighbors, support vector machines, and neural networks, are useful for classification. However, these methods provide no insight regarding the covariates that best contribute to the predictive structure. Other methods, such as linear discriminant analysis, require the predictor space be substantially reduced prior to deriving the classifier. A recently developed method, random forests (RF), does not require reduction of the predictor space prior to classification. Additionally, RF yield variable importance measures for each candidate predictor. This study examined the effectiveness of RF variable importance measures in identifying the true predictor among a large number of candidate predictors. An extensive simulation study was conducted using 20 levels of correlation among the predictor variables and 7 levels of association between the true predictor and the dichotomous response. We conclude that the RF methodology is attractive for use in classification problems when the goals of the study are to produce an accurate classifier and to provide insight regarding the discriminative ability of individual predictor variables. Such goals are common among microarray studies, and therefore application of the RF methodology for the purpose of obtaining variable importance measures is demonstrated on a microarray data set.
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ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2007.08.015