Variable selection using random forests

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

The Elements of Statistical Learning

Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162

Classification and Regression Trees.

Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867

Classification and Regression Trees.

Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030

Classification and Regression by randomForest

Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594

Bagging predictors

Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325

Principal Component Analysis

Jolliffe, I. T. (2005) · Wiley eBooks · doi:10.1002/0470013192.bsa501 · Cited by 14,522

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring

Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., et al. (1999) · Science · doi:10.1126/science.286.5439.531 · Cited by 11,516

Gene selection for cancer classification using support vector machines

Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954

Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling

Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., Boldrick, J. C., Sabet, H., Tran, T., Yu, X., et al. (2000) · Nature · doi:10.1038/35000501 · Cited by 9,925

Least angle regression

Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004) · The Annals of Statistics · doi:10.1214/009053604000000067 · Cited by 9,357

Wrappers for feature subset selection

Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 9,096

Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties

B, J. F. A., & Li, R. (2001) · Journal of the American Statistical Association · doi:10.1198/016214501753382273 · Cited by 8,919

Ensemble methods in machine learning

Dietterich, T. G. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45014-9_1 · Cited by 8,408

An introduction to variable and feature selection

GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 8,354

The random subspace method for constructing decision forests

Ho, T. K. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.709601 · Cited by 7,005

An Introduction to Variable and Feature Selection.

Guyon, I., & Elisseeff, A. (2003) · Cited by 6,576

Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays

Alon, U. S., Barkai, N., Notterman, D. A., Gish, K., Ybarra, S. E., Mack, D. H., & Levine, A. J. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.12.6745 · Cited by 4,170

Bias in random forest variable importance measures: Illustrations, sources and a solution

Strobl, C., Boulesteix, A., Zeileis, A., & Hothorn, T. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-25 · Cited by 3,970

Conditional variable importance for random forests

Strobl, C., Boulesteix, A., Kneib, T., Augustin, T., & Zeileis, A. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-307 · Cited by 3,467

Gene selection and classification of microarray data using random forest

Díaz‐Uriarte, R., & de Andrés, S. Á. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-3 · Cited by 3,013

An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

Dietterich, T. G. (2000) · Machine Learning · doi:10.1023/a:1007607513941 · Cited by 2,996

Sensitivity Estimates for Nonlinear Mathematical Models

Sobol, I. M. (1990) · Medical Entomology and Zoology · Cited by 2,806

Sure Independence Screening for Ultrahigh Dimensional Feature Space

Fan, J., & Lv, J. (2008) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2008.00674.x · Cited by 2,643

Relative Importance for Linear Regression in R : The Package relaimpo

Grömping, U. (2006) · Journal of Statistical Software · doi:10.18637/jss.v017.i01 · Cited by 2,615

Gene expression correlates of clinical prostate cancer behavior

Singh, D., Febbo, P. G., Ross, K. N., Jackson, D. G., Manola, J. B., Ladd, C., Tamayo, P., Renshaw, A. A., D'Amico, A. V., Richie, J. P., et al. (2002) · Cancer Cell · doi:10.1016/s1535-6108(02)00030-2 · Cited by 2,516

Empirical characterization of random forest variable importance measures

Archer, K. J., & Kimes, R. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.08.015 · Cited by 1,162

Variable Importance Assessment in Regression: Linear Regression versus Random Forest

Grömping, U. (2009) · The American Statistician · doi:10.1198/tast.2009.08199 · Cited by 1,102

L 1-Regularization Path Algorithm for Generalized Linear Models

Park, M. Y., & Hastie, T. J. (2007) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2007.00607.x · Cited by 859

Classification and regression

Bishop, C. (2004) · Institute of Physics eBooks · doi:10.1887/0750303123/b365c43 · Cited by 859

Use of the zero norm with linear models and kernel methods

Weston, J., Elisseeff, A., Schölkopf, B., & Tipping, M. (2003) · MPG.PuRe (Max Planck Society) · doi:10.5555/944919.944982 · Cited by 692

Sensitivity analysis for non-linear mathematical models

Sobol, I. M. (1993) · Medical Entomology and Zoology · Cited by 644

Estimators of Relative Importance in Linear Regression Based on Variance Decomposition

Grömping, U. (2007) · The American Statistician · doi:10.1198/000313007x188252 · Cited by 643

Analyzing bagging

Bühlmann, P., & Yu, B. (2002) · The Annals of Statistics · doi:10.1214/aos/1031689014 · Cited by 635

Variable selection using svm based criteria

Rakotomamonjy, A. (2003) · doi:10.5555/944919.944977 · Cited by 616

Consistency of Random Forests and Other Averaging Classifiers

Gérard Biau, A., Devroye, L., & Lugosi, G. (2007) · Cited by 457

Random Forests: some methodological insightsPreprint

Genuer, R., Poggi, J., & Tuleau-Malot, C. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0811.3619 · Cited by 204

CONSISTENCY FOR A SIMPLE MODEL OF RANDOM FORESTS

Breiman, L. (2004) · Cited by 165

Random Forests based feature selection for decoding fMRI data

Genuer, R., Michel, V., Eger, E., & Thirion, B. (2010) · HAL · Cited by 23

Sélection de variables pour la classification binaire en grande dimension: comparaisons et application aux domées de biopuces

Ghattas, B., & Ishak, A. B. (2008) · HAL · Cited by 13

Classification supervisée en grande dimension. Application à l'agrément de conduite automobilePreprint

Poggi, J., & Tuleau-Malot, C. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1010.6227 · Cited by 10

Partial and Recombined Estimators for Nonlinear Additive Models

Chèze, N., Poggi, J., & Portier, B. (2003) · Statistical Inference for Stochastic Processes · doi:10.1023/a:1023940117323 · Cited by 9

Gene Selection and Classification

(2008) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789812790743_0007 · Cited by 7

From local explanations to global understanding with explainable AI for trees

Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0138-9 · Cited by 10,551

A random forest guided tour

Biau, G., & Scornet, E. (2016) · Test · doi:10.1007/s11749-016-0481-7 · Cited by 4,149

Hyperparameters and tuning strategies for random forest

Probst, P., Wright, M. N., & Boulesteix, A. (2019) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1301 · Cited by 2,319

Leadership, social capital and incentives promote successful fisheries

Gutiérrez, N. L., Hilborn, R. W., & Defeo, O. (2011) · Nature · doi:10.1038/nature09689 · Cited by 1,441

A review of feature selection methods with applications

Jović, A., Brkić, K., & Bogunovic, N. (2015) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro.2015.7160458 · Cited by 1,238

Random Forests: some methodological insightsPreprint

Genuer, R., Poggi, J., & Tuleau-Malot, C. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0811.3619 · Cited by 204

Random Forests Variable Selection

Ishwaran, H. (2025) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003493679-17

Random forests variable importances Towards a better understanding and large-scale feature selection

Sutera, A. (2016) · ORBi (University of Liège)

Identification of Statistically Significant Features from Random Forests

Paul, J., Verleysen, M., & Dupont, P. (2013) · Cited by 10

VSURF: An R Package for Variable Selection Using Random Forests

Genuer, R., Poggi, J., & Tuleau-Malot, C. (2015) · The R Journal · doi:10.32614/rj-2015-018 · Cited by 633