A computationally fast variable importance test for random forests for high-dimensional data
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DOI: 10.1007/s11634-016-0276-4
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Cited by:
- 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.
- 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).
- 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).
- 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.
- 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.
- 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.
- Massimiliano Fessina & Giambattista Albora & Andrea Tacchella & Andrea Zaccaria, 2022. "Which products activate a product? An explainable machine learning approach," Papers 2212.03094, arXiv.org.
- 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.
- 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).
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Keywords
Gene selection; Feature selection; Random forests; Variable importance; Variable selection; Variable importance test;All these keywords.
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