A computationally fast variable importance test for random forests for high-dimensional data
Author
Abstract
Suggested Citation
DOI: 10.1007/s11634-016-0276-4
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Ruoqing Zhu & Donglin Zeng & Michael R. Kosorok, 2015. "Reinforcement Learning Trees," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1770-1784, December.
- Janitza, Silke & Tutz, Gerhard & Boulesteix, Anne-Laure, 2016. "Random forest for ordinal responses: Prediction and variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 57-73.
- Phipson Belinda & Smyth Gordon K, 2010. "Permutation P-values Should Never Be Zero: Calculating Exact P-values When Permutations Are Randomly Drawn," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-16, October.
- Anne-Laure Boulesteix, 2015. "Ten Simple Rules for Reducing Overoptimistic Reporting in Methodological Computational Research," PLOS Computational Biology, Public Library of Science, vol. 11(4), pages 1-6, April.
- Hapfelmeier, A. & Ulm, K., 2013. "A new variable selection approach using Random Forests," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 50-69.
- Kim H. & Loh W.Y., 2001. "Classification Trees With Unbiased Multiway Splits," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 589-604, June.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Hapfelmeier, Alexander & Hornung, Roman & Haller, Bernhard, 2023. "Efficient permutation testing of variable importance measures by the example of random forests," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
- Riccardo Rosati & Luca Romeo & Gianalberto Cecchini & Flavio Tonetto & Paolo Viti & Adriano Mancini & Emanuele Frontoni, 2023. "From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 107-121, January.
- Oyebayo Ridwan Olaniran & Ali Rashash R. Alzahrani, 2023. "On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression," Mathematics, MDPI, vol. 11(24), pages 1-29, December.
- Gordeev, Stepan & Steinbach, Sandro, 2024. "Determinants of PTA design: Insights from machine learning," International Economics, Elsevier, vol. 178(C).
- KONDO Satoshi & MIYAKAWA Daisuke & SHIRAKI Kengo & SUGA Miki & USUKI Teppei, 2019. "Using Machine Learning to Detect and Forecast Accounting Fraud," Discussion papers 19103, Research Institute of Economy, Trade and Industry (RIETI).
- Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021.
"Machine learning and oil price point and density forecasting,"
Energy Economics, Elsevier, vol. 102(C).
- Alexandre Bonnet R. Costa & Pedro Cavalcanti G. Ferreira & Wagner P. Gaglianone & Osmani Teixeira C. Guillén & João Victor Issler & Yihao Lin, 2021. "Machine Learning and Oil Price Point and Density Forecasting," Working Papers Series 544, Central Bank of Brazil, Research Department.
- Hornung, Roman & Boulesteix, Anne-Laure, 2022. "Interaction forests: Identifying and exploiting interpretable quantitative and qualitative interaction effects," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
- Araujo, Gustavo Silva & Gaglianone, Wagner Piazza, 2023.
"Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models,"
Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(2).
- Gustavo Silva Araujo & Wagner Piazza Gaglianone, 2022. "Machine Learning Methods for Inflation Forecasting in Brazil: new contenders versus classical models," Working Papers Series 561, Central Bank of Brazil, Research Department.
- Massimiliano Fessina & Giambattista Albora & Andrea Tacchella & Andrea Zaccaria, 2022. "Which products activate a product? An explainable machine learning approach," Papers 2212.03094, arXiv.org.
- Hediger, Simon & Michel, Loris & Näf, Jeffrey, 2022. "On the use of random forest for two-sample testing," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
- Jin Yutong & Benkeser David, 2022. "Identifying HIV sequences that escape antibody neutralization using random forests and collaborative targeted learning," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 280-295, January.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
- Strobl, Carolin & Boulesteix, Anne-Laure & Augustin, Thomas, 2007. "Unbiased split selection for classification trees based on the Gini Index," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 483-501, September.
- Weijun Wang & Dan Zhao & Liguo Fan & Yulong Jia, 2019. "Study on Icing Prediction of Power Transmission Lines Based on Ensemble Empirical Mode Decomposition and Feature Selection Optimized Extreme Learning Machine," Energies, MDPI, vol. 12(11), pages 1-21, June.
- Ruoqing Zhu & Ying-Qi Zhao & Guanhua Chen & Shuangge Ma & Hongyu Zhao, 2017. "Greedy outcome weighted tree learning of optimal personalized treatment rules," Biometrics, The International Biometric Society, vol. 73(2), pages 391-400, June.
- Lkhagvadorj Munkhdalai & Tsendsuren Munkhdalai & Oyun-Erdene Namsrai & Jong Yun Lee & Keun Ho Ryu, 2019. "An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments," Sustainability, MDPI, vol. 11(3), pages 1-23, January.
- Ollech, Daniel & Webel, Karsten, 2020. "A random forest-based approach to identifying the most informative seasonality tests," Discussion Papers 55/2020, Deutsche Bundesbank.
- Pedro Delicado & Daniel Peña, 2023. "Understanding complex predictive models with ghost variables," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 107-145, March.
- Shih, Y. -S., 2004. "A note on split selection bias in classification trees," Computational Statistics & Data Analysis, Elsevier, vol. 45(3), pages 457-466, April.
- Gairaa, Kacem & Voyant, Cyril & Notton, Gilles & Benkaciali, Saïd & Guermoui, Mawloud, 2022. "Contribution of ordinal variables to short-term global solar irradiation forecasting for sites with low variabilities," Renewable Energy, Elsevier, vol. 183(C), pages 890-902.
- repec:hum:wpaper:sfb649dp2008-035 is not listed on IDEAS
- José A. Ferreira, 2022. "Models under which random forests perform badly; consequences for applications," Computational Statistics, Springer, vol. 37(4), pages 1839-1854, September.
- Zardad Khan & Asma Gul & Aris Perperoglou & Miftahuddin Miftahuddin & Osama Mahmoud & Werner Adler & Berthold Lausen, 2020. "Ensemble of optimal trees, random forest and random projection ensemble classification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(1), pages 97-116, March.
- Susan Athey & Julie Tibshirani & Stefan Wager, 2016.
"Generalized Random Forests,"
Papers
1610.01271, arXiv.org, revised Apr 2018.
- Athey, Susan & Tibshirani, Julie & Wager, Stefan, 2017. "Generalized Random Forests," Research Papers 3575, Stanford University, Graduate School of Business.
- Chou, Yuntsai & Lin, Wei, 2024. "Blockbuster or Flop? Effects of Social Media on the Chinese Film Market," 24th ITS Biennial Conference, Seoul 2024. New bottles for new wine: digital transformation demands new policies and strategies 302460, International Telecommunications Society (ITS).
- Hothorn, Torsten & Lausen, Berthold, 2005. "Bundling classifiers by bagging trees," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1068-1078, June.
- Liu, Yehong & Yin, Guosheng, 2020. "The Delaunay triangulation learner and its ensembles," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
- Fellinghauer, Bernd & Bühlmann, Peter & Ryffel, Martin & von Rhein, Michael & Reinhardt, Jan D., 2013. "Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 132-152.
- Ha, Tran Vinh & Asada, Takumi & Arimura, Mikiharu, 2019. "Determination of the influence factors on household vehicle ownership patterns in Phnom Penh using statistical and machine learning methods," Journal of Transport Geography, Elsevier, vol. 78(C), pages 70-86.
- Hermel Homburger & Manuel K Schneider & Sandra Hilfiker & Andreas Lüscher, 2014. "Inferring Behavioral States of Grazing Livestock from High-Frequency Position Data Alone," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-22, December.
- Emilio Carrizosa & Cristina Molero-Río & Dolores Romero Morales, 2021. "Mathematical optimization in classification and regression trees," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 5-33, April.
- Ingrida Vaiciulyte & Zivile Kalsyte & Leonidas Sakalauskas & Darius Plikynas, 2017. "Assessment of market reaction on the share performance on the basis of its visualization in 2D space," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(2), pages 309-318, March.
More about this item
Keywords
Gene selection; Feature selection; Random forests; Variable importance; Variable selection; Variable importance test;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:advdac:v:12:y:2018:i:4:d:10.1007_s11634-016-0276-4. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.