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Empirical Characterization of Random Forest Variable Importance Measures by Kellie J. Archer; Ryan V. Kimes is a Mathematics article available to read on EtoBox.

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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 l

Who reads Empirical Characterization of Random Forest Variable Importance Measures?

Author

Kellie J. Archer; Ryan V. Kimes

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0167-9473)

Published

2008

Language

EN

Field

Mathematics (Physical Sciences)