Improvements to random forest methodology

Keywords

Debiasing, Random forest, Variable (mathematics), Computer science, Machine learning

References

• Classification and Regression by randomForestAndy Liaw, Matthew C. Wiener — 2007
• The Estimation of Prediction ErrorBradley Efron — 2004
• Quantile Regression ForestsNicolai Meinshausen — 2006
• Prediction of Adverse Drug Reactions Using Decision Tree ModelingFelix Hammann, Heike Gutmann, Nicole P. Vogt — 2010
• Short-term prediction of mortality in patients with systemic lupus erythematosus: classification of outcomes using random forests.Michael M. Ward, Sinisa Pajevic, Jonathan M. Dreyfuss — 2006
• Random Forest Models To Predict Aqueous SolubilityDavid Scott Palmer, Noel M. O’Boyle, Robert C. Glen — 2007
• Enriched random forestsDhammika Amaratunga, Javier Cabrera, Yung-Seop Lee — 2008
• Navigating Random Forests and related advances in algorithmic modelingDavid S. Siroky — 2009
• Random Forests and Adaptive Nearest NeighborsYi Lin, Yongho Jeon — 2006
• Random survival forestsHemant Ishwaran, Udaya B. Kogalur, Eugene H. Blackstone — 2008
• Extremely randomized treesPierre Geurts, Damien Ernst, Louis Wehenkel — 2006
• Remembering Leo BreimanRichard A. Olshen — 2010
• On bagging and nonlinear estimationJerome H. Friedman, Peter Hall — 2007
• Heuristics of instability and stabilization in model selectionLeo Breiman — 1996
• Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)Leo Breiman — 2001
• Multivariate random forestsMark R. Segal, Yuanyuan Xiao — 2011
• The Random Subspace Method for Constructing Decision ForestsTin Kam Ho — 1998
• MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined featuresPeng Jiang, H. Wu, Wenxue Wang — 2007
• Tumor classification by tissue microarray profiling: random forest clustering applied to renal cell carcinomaTao Shi, David B. Seligson, Arie S. Belldegrun — 2005
• Shape Quantization and Recognition with Randomized TreesYali Amit, Donald Geman — 1997

Cited by

• Some Bayesian and multivariate analysis methods in statistical machine learning and applicationsWen Zhou — 2014
• Two-level quantile regression forests for bias correction in range predictionThanh-Tung Nguyen, Joshua Zhexue Huang, Nguyễn Thị Thanh Thủy — 2015
• Comparison of Multivariate Adaptive Regression Splines and Random Forest Regression in Predicting Forced Expiratory Volume in One SecondP. V. Pramila, Vijayalakshmi G. V. Mahesh — 2015
• Mapping sub-pixel urban expansion in China using MODIS and DMSP/OLS nighttime lightsXiaoman Huang, Annemarie Schneider, M. A. Friedl — 2016
• Influence of cognitive, geographical, and collaborative proximity on knowledge production of Canadian nanotechnologyCrespo Neira, Elva Luz — 2016
• Factors Associated with HIV Testing Among Participants from Substance Use Disorder Treatment Programs in the US: A Machine Learning ApproachYue Pan, Hongmei Liu, Lisa R. Metsch — 2017
• A frequency-based gene selection method with random forests for gene data analysisThanh Trinh, Dingming Wu, Salman Salloum — 2016
• Classification algorithms for body postureSílvia Silva, Sandro Queirós, António H. J. Moreira — 2017
• Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and Its Variance EstimateIndrayudh Ghosal, Giles Hooker — 2018
• Energy efficient human activity recognition using wearable sensorsGenming Ding, Junfang Tian, Jinsong Wu — 2018
• The utility of spatial model‐based estimators of unobserved bycatchBrian C. Stock, Eric J. Ward, James T. Thorson — 2018
• Supervised heterogeneous feature transfer via random forestsSanatan Sukhija, Narayanan C. Krishnan — 2019
• Probability of Streamflow Permanence Model (PROSPER): A spatially continuous model of annual streamflow permanence throughout the Pacific NorthwestKristin L. Jaeger, Roy Sando, Ryan R. McShane — 2019
• Forest Site and Type Variability in ALS-Based Forest Resource Inventory Attribute Predictions over Three Ontario Forest SitesKarin van Ewijk, Paul Treitz, Murray Woods — 2019
• A Survey of Techniques All Classifiers Can Learn from Deep Networks: Models, Optimizations, and RegularizationAlireza Ghods, Diane J. Cook — 2019
• Using Artificial Intelligence Techniques to Improve the Prediction of Copper Recovery by LeachingVíctor Flores, Brian Keith, Claudio Leiva — 2020
• A machine learning approach for allocating route cost to customers for transportation and logistics services.Alison Davis — 2020
• A Survey of Deep Network Techniques All Classifiers Can AdoptAlireza Ghods, Diane J. Cook — 2020
• Considering Spatiotemporal Forage Variability in Rangeland Inventory and MonitoringScott N. Zimmer, Eugene W. Schupp, Janis L. Boettinger — 2021
• Deep and machine learning approaches for forecasting the residual value of heavy construction equipment: a management decision support modelOdey Alshboul, Ali Shehadeh, Maha Al-Kasasbeh — 2021

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