A random forest guided tour

Keywords

Random forest, Resampling, Computer science, Feature selection, Variable (mathematics)

References

• Reinforcement Learning TreesTomas Landelius, Magnus Borga, Hans Knutsson — 1996
• Classification and Regression by randomForestAndy Liaw, Matthew C. Wiener — 2007
• Applied Predictive ModelingMax Kühn, Kjell Johnson — 2013
• A Novel Test for Additivity in Supervised Ensemble LearnersLucas Mentch, Giles Hooker — 2014
• Multiple Classifier SystemsFabio Roli, Josef Kittler, Terry Windeatt — 2004
• The Elements of Statistical Learning: Data Mining, Inference, and PredictionTrevor Hastie, J. Friedman, Robert Tibshirani — 2001
• Approximate False Positive Rate Control in Selection Frequency for Random ForestEnder Konukoğlu, Melanie Ganz — 2014
• A Distribution-Free Theory of Nonparametric RegressionLászló Györfi, Michael Köhler, Adam Krzyżak — 2002
• Asymptotic Theory for Random ForestsStefan Wager — 2014
• Mining data with random forests: current options for real‐world applicationsAndreas Ziegler, Inke R. König — 2014
• The Random Forest Kernel and other kernels for big data from random partitionsAlex Davies, Zoubin Ghahramani — 2014
• Gene selection and classification of microarray data using random forestRamón Díaz‐Uriarte, Sara Álvarez de Andrés — 2006
• Multiple Classifier SystemsJosef Kittler, Fabio Roli — 2000
• A Probabilistic Theory of Pattern RecognitionLuc Devroye, László Györfi, Gábor Lugosi — 1996
• Quantile Regression ForestsNicolai Meinshausen — 2006
• Randomizing Outputs to Increase Prediction AccuracyLeo Breiman — 2000
• Classification and regression treesAlexander Gordon, Leo Breiman, Jerome H. Friedman — 1983
• Probability estimation with machine learning methods for dichotomous and multicategory outcome: ApplicationsJochen Kruppa, Yufeng Liu, Hans‐Christian Diener — 2014
• Random Forests with Missing Values in the CovariatesAnna Rieger, Torsten Hothorn, Carolin Strobl — 2010
• Fast growing and interpretable oblique trees via logistic regression modelsAlfred Kar Yin Truong — 2009

Cited by

• Big Data: New Tricks for EconometricsHal R. Varian — 2014
• Comments on: "A Random Forest Guided Tour" by G. Biau and E. ScornetSylvain Arlot, Robin Genuer — 2016
• Neural Random ForestsGérard Biau, Erwan Scornet, Johannes Welbl — 2016
• Unbiased split variable selection for random survival forests using maximally selected rank statisticsMarvin N. Wright, Theresa Dankowski, Andreas Ziegler — 2016
• Landscape anthropometrics: A multi-scale approach to integrating health into the regional landscapeArthi Vijayanagara Rao — 2016
• Solving Heterogeneous Estimating Equations with Gradient ForestsSusan Athey, Julie Tibshirani, Stefan Wager — 2016
• A novel framework for analyzing MOS E-nose data based on voting theory: Application to evaluate the internal quality of Chinese pecansShui Jiang, Jun Wang, Yongwei Wang — 2017
• One Class Splitting Criteria for Random ForestsNicolas Goix, Nicolas Drougard, Romain Brault — 2016
• Wavelet decompositions of Random Forests - smoothness analysis, sparse approximation and applicationsOren Elisha, Shai Dekel — 2016
• Decision trees and forests: a probabilistic perspectiveBalaji Lakshminarayanan — 2016
• Classification and identification of small objects in complex urban-forested LIDAR data using machine learningWilliam Basener, Abigail Basener — 2017
• Optimization of Tree EnsemblesVelibor V. Mišić — 2017
• Metalearners for estimating heterogeneous treatment effects using machine learningSören R. Künzel, Jasjeet S. Sekhon, Peter J. Bickel — 2017
• Random survival forest with space extensions for censored dataHong Wang, Lifeng Zhou — 2017
• Data-driven Weld Nugget Width Prediction with Decision Tree AlgorithmFahim Ahmed, Kyoung‐Yun Kim — 2017
• Generalized random forestsSusan Athey, Julie Tibshirani, Stefan Wager — 2016
• Assessing the suitability of data from Sentinel-1A and 2A for crop classificationRei Sonobe, Yuki Yamaya, Hiroshi Tani — 2017
• Large-scale operator-valued kernel regression. (Régression à noyaux à valeurs opérateurs pour grands ensembles de données)Romain Brault — 2017
• Data Based Prediction of Blood Glucose Concentrations Using Evolutionary MethodsJ. Ignacio Hidalgo, J. Manuel Colmenar, Gabriel Kronberger — 2017
• A Novel Consistent Random Forest Framework: Bernoulli Random ForestsYisen Wang, Shu‐Tao Xia, Qingtao Tang — 2018

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