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Random forest for ordinal responses: Prediction and variable selection

Citations

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Cited by:

  1. Wang, Yong & Ma, Yinjie & Xie, Deyi & Yu, Zhenhuan & E, Jiaqiang, 2021. "Numerical study on the influence of gasoline properties and thermodynamic conditions on premixed laminar flame velocity at multiple conditions," Energy, Elsevier, vol. 233(C).
  2. Esenyel İçen, Nimet Melis, 2025. "What are the determinants of renewable energy consumption? An application for variable selection," Renewable Energy, Elsevier, vol. 239(C).
  3. Roman Hornung, 2020. "Ordinal Forests," Journal of Classification, Springer;The Classification Society, vol. 37(1), pages 4-17, April.
  4. Aleix Alcacer & Irene Epifanio & Jorge Valero & Alfredo Ballester, 2021. "Combining Classification and User-Based Collaborative Filtering for Matching Footwear Size," Mathematics, MDPI, vol. 9(7), pages 1-15, April.
  5. Marcella Corduas & Alfonso Piscitelli, 2017. "Modeling university student satisfaction: the case of the humanities and social studies degree programs," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 617-628, March.
  6. 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.
  7. repec:plo:pone00:0210426 is not listed on IDEAS
  8. Yifei Jiang & Honglei Zhang & Xianting Cao & Ge Wei & Yang Yang, 2023. "How to better incorporate geographic variation in Airbnb price modeling?," Tourism Economics, , vol. 29(5), pages 1181-1203, August.
  9. Michael Lechner & Gabriel Okasa, 2025. "Random Forest estimation of the ordered choice model," Empirical Economics, Springer, vol. 68(1), pages 1-106, January.
  10. Yaser Abdollahfard & Mehdi Sedighi & Mostafa Ghasemi, 2023. "A New Approach for Improving Microbial Fuel Cell Performance Using Artificial Intelligence," Sustainability, MDPI, vol. 15(2), pages 1-14, January.
  11. repec:osf:osfxxx:ny6we_v1 is not listed on IDEAS
  12. Yiwei Fan & Jiaqi Gu & Guosheng Yin, 2023. "Sparse concordance‐based ordinal classification," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(3), pages 934-961, September.
  13. Xuepeng Guo & Linyan Liu & HuiFen Wang & Yue Li & XiaoDong Du & JianCheng Shi & Yue Wang, 2025. "An online prediction method for array antenna assembly performance based on digital twin," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2727-2748, April.
  14. Maljkovic, Danica & Basic, Bojana Dalbelo, 2020. "Determination of influential parameters for heat consumption in district heating systems using machine learning," Energy, Elsevier, vol. 201(C).
  15. Silke Janitza & Ender Celik & Anne-Laure Boulesteix, 2018. "A computationally fast variable importance test for random forests for high-dimensional data," 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. 12(4), pages 885-915, December.
  16. Gerhard Tutz, 2022. "Ordinal Trees and Random Forests: Score-Free Recursive Partitioning and Improved Ensembles," Journal of Classification, Springer;The Classification Society, vol. 39(2), pages 241-263, July.
  17. 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.
  18. Buczak, Philip & Horn, Daniel & Pauly, Markus, 2024. "Old but Gold or New and Shiny? Comparing Tree Ensembles for Ordinal Prediction with a Classic Parametric Approach," OSF Preprints v7bcf, Center for Open Science.
  19. repec:osf:osfxxx:v7bcf_v1 is not listed on IDEAS
  20. Guoqiang Chen & Tianyu Long & Jiangong Xiong & Yun Bai, 2017. "Multiple Random Forests Modelling for Urban Water Consumption Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4715-4729, December.
  21. Mohammad Mehedy Hassan & Jane Southworth, 2017. "Analyzing Land Cover Change and Urban Growth Trajectories of the Mega-Urban Region of Dhaka Using Remotely Sensed Data and an Ensemble Classifier," Sustainability, MDPI, vol. 10(1), pages 1-24, December.
  22. Riccardo Di Francesco, 2023. "Ordered Correlation Forest," Papers 2309.08755, arXiv.org.
  23. Buczak, Philip, 2024. "Mixed-Effects Frequency-Adjusted Borders Ordinal Forest: A Tree Ensemble Method for Ordinal Prediction with Hierarchical Data," OSF Preprints ny6we, Center for Open Science.
  24. Weidong Guo & Zach Zhizhong Zhou, 2022. "A comparative study of combining tree‐based feature selection methods and classifiers in personal loan default prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1248-1313, September.
  25. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
  26. repec:osf:osfxxx:h8t4p_v1 is not listed on IDEAS
  27. Apostolos G. Katsafados & Dimitris Anastasiou, 2024. "Short-term prediction of bank deposit flows: do textual features matter?," Annals of Operations Research, Springer, vol. 338(2), pages 947-972, July.
  28. Odey Alshboul & Ali Shehadeh & Ghassan Almasabha & Ali Saeed Almuflih, 2022. "Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction," Sustainability, MDPI, vol. 14(11), pages 1-20, May.
  29. Philip Buczak & Daniel Horn & Markus Pauly, 2025. "Old but Gold or New and Shiny? Comparing Tree Ensembles for Ordinal Prediction with a Classic Parametric Approach," Journal of Classification, Springer;The Classification Society, vol. 42(2), pages 364-390, July.
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