Fetching the paper…
Reading the bibliography…
We propose a modification that corrects for split-improvement variable importance measures in Random Forests and other tree-based methods.
Book Review: Regression Diagnostics: Identifying Influential Data and Sources of Collinearity
Galen Bollinger. 1981 · 1981
Earlier work this paper cites.
Classification and Regression Trees
L Breiman, JH Friedman, R Olshen, and CJ Stone. 1984 · 1984
Earlier work this paper cites.
Combining instance-based and model-based learning. In Proceedings of the tenth international conference on machine learning . 236–243
J Ross Quinlan. 1993 · 1993
Earlier work this paper cites.
Bagging predictors
Leo Breiman. 1996 · 1996
Earlier work this paper cites.
Split selection methods for classification trees
Wei-Yin Loh and Yu-Shan Shih. 1997 · 1997
Earlier work this paper cites.
Random forests
Leo Breiman. 2001 · 2001
Earlier work this paper cites.
The elements of statistical learning . Vol. 1
Jerome Friedman, Trevor Hastie, and Robert Tibshirani. 2001 · 2001
Earlier work this paper cites.
Greedy function approximation: a gradient boosting machine
Jerome H Friedman. 2001 · 2001
Earlier work this paper cites.
Classification trees with unbiased multiway splits
Hyunjoong Kim and Wei-Yin Loh. 2001 · 2001
Earlier work this paper cites.
Simple statistical models predict C-to-U edited sites in plant mitochondrial RNA
Michael P Cummings and Daniel S Myers. 2004 · 2004
Earlier work this paper cites.
Gene selection and classification of microarray data using random forest
Ramón Díaz-Uriarte and Sara Alvarez De Andres. 2006 · 2006
Earlier work this paper cites.
Unbiased recursive partitioning: A conditional inference framework
Torsten Hothorn, Kurt Hornik, and Achim Zeileis. 2006 · 2006
Earlier work this paper cites.
Generalized functional anova diagnostics for high-dimensional functions of dependent variables
Giles Hooker. 2007 · 2007
Earlier work this paper cites.
Variable importance in binary regression trees and forests
Hemant Ishwaran et al · 2007
Earlier work this paper cites.
Bias in random forest variable importance measures: Illustrations, sources and a solution
Carolin Strobl, Anne-Laure Boulesteix, Achim Zeileis, and Torsten Hothorn. 2007 · 2007
Earlier work this paper cites.
Random survival forests
Hemant Ishwaran, Udaya B Kogalur, Eugene H Blackstone, Michael S Lauer, et al · 2008
Cited alongside, same era.
A bias correction algorithm for the Gini variable importance measure in classification trees
Marco Sandri and Paola Zuccolotto. 2008 · 2008
Cited alongside, same era.
Conditional variable importance for random forests
Carolin Strobl, Anne-Laure Boulesteix, Thomas Kneib, Thomas Augustin, and Achim Zeileis. 2008 · 2008
Cited alongside, same era.
Improving the precision of classification trees
Wei-Yin Loh et al · 2009
Cited alongside, same era.
A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data
Bjoern H Menze, B Michael Kelm, Ralf Masuch, Uwe Himmelreich, Peter Bachert, Wolfgang Petrich, and Fred A Hamprecht. 2009 · 2009
Cited alongside, same era.
Party: A laboratory for recursive partytioning
Quantifying uncertainty in random forests via confidence intervals and hypothesis tests
Lucas Mentch and Giles Hooker. 2016 · 2016
Later among the works it cites.
Amazon Search: The Joy of Ranking Products. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval . ACM, 459–460
Daria Sorokina and Erick Cantú-Paz. 2016 · 2016
Later among the works it cites.
Bernoulli random forests: Closing the gap between theoretical consistency and empirical soundness. In IJCAI International Joint Conference on Artificial Intelligence
Wang Yisen, Tang Qingtao, Shu-Tao Xia, Jia Wu, and Xingquan Zhu. 2016 · 2016
Later among the works it cites.
Correlation and variable importance in random forests
Baptiste Gregorutti, Bertrand Michel, and Philippe Saint-Pierre. 2017 · 2017
Later among the works it cites.
Interactome INSIDER: A Multi-Scale Structural Interactome Browser For Genomic Studies
Michael Meyer, Juan Felipe Beltrán, Siqi Liang, Robert Fragoza, Aaron Rumack, Jin Liang, Xiaomu Wei, and Haiyuan Yu. 2017 · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Torsten Hothorn, Kurt Hornik, Carolin Strobl, and Achim Zeileis. 2010 · 2010
Cited alongside, same era.
Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations
Anne-Laure Boulesteix, Andreas Bender, Justo Lorenzo Bermejo, and Carolin Strobl. 2011 · 2011
Cited alongside, same era.
Letter to the editor: On the stability and ranking of predictors from random forest variable importance measures
Kristin K Nicodemus. 2011 · 2011
Cited alongside, same era.
Scikit-learn: Machine learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
Cited alongside, same era.
Narrowing the gap: Random forests in theory and in practice. In International conference on machine learning . 665–673
Misha Denil, David Matheson, and Nando De Freitas. 2014 · 2014
Cited alongside, same era.
Fifty years of classification and regression trees
Wei-Yin Loh. 2014 · 2014
Cited alongside, same era.
C4. 5: programs for machine learning
J Ross Quinlan. 2014 · 2014
Cited alongside, same era.
Later among the works it cites.
A novel consistent random forest framework: Bernoulli random forests
Yisen Wang, Shu-Tao Xia, Qingtao Tang, Jia Wu, and Xingquan Zhu. 2017 · 2017
Later among the works it cites.
Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and its Variance Estimate
Indrayudh Ghosal and Giles Hooker. 2018 · 2018
Later among the works it cites.
The revival of the Gini importance?
Stefano Nembrini, Inke R König, and Marvin N Wright. 2018 · 2018
Later among the works it cites.
Estimation and inference of heterogeneous treatment effects using random forests
Stefan Wager and Susan Athey. 2018 · 2018
Later among the works it cites.
Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution
Yichen Zhou and Giles Hooker. 2018 · 2018
Later among the works it cites.
Approximation Trees: Statistical Stability in Model Distillation
Yichen Zhou, Zhengze Zhou, and Giles Hooker. 2018 · 2018
Later among the works it cites.
Please Stop Permuting Features: An Explanation and Alternatives
Giles Hooker and Lucas Mentch. 2019 · 2019
Closest in time.
A Debiased MDI Feature Importance Measure for Random Forests
Xiao Li, Yu Wang, Sumanta Basu, Karl Kumbier, and Bin Yu. 2019 · 2019
Closest in time.
Controlling the false discovery rate via knockoffs
Rina Foygel Barber, Emmanuel J Candès, et al · 2085
Closest in time.