Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Bagging and the Random Subspace Method for Redundant Feature Spaces
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Boosting, Bagging, and Consensus Based Classification of Multisource Remote Sensing Data
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Bagging tree classifiers for laser scanning images: a data- and simulation-based strategy
Artificial Intelligence in Medicine
A similarity measure to assess the stability of classification trees
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Ensemble classification based on generalized additive models
Computational Statistics & Data Analysis
Variable selection using random forests
Pattern Recognition Letters
Mining data with random forests: A survey and results of new tests
Pattern Recognition
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
A new variable selection approach using Random Forests
Computational Statistics & Data Analysis
A new variable importance measure for random forests with missing data
Statistics and Computing
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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 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.