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Unbiased split selection for classification trees based on the Gini Index

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Cited by:

  1. Archer, Kellie J. & Kimes, Ryan V., 2008. "Empirical characterization of random forest variable importance measures," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2249-2260, January.
  2. 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).
  3. Hapfelmeier, A. & Hothorn, T. & Ulm, K., 2012. "Recursive partitioning on incomplete data using surrogate decisions and multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1552-1565.
  4. Burim Ramosaj & Markus Pauly, 2019. "Predicting missing values: a comparative study on non-parametric approaches for imputation," Computational Statistics, Springer, vol. 34(4), pages 1741-1764, December.
  5. Jinglun Yao & Maxime Levy-Chapira & Mamikon Margaryan, 2017. "Checking account activity and credit default risk of enterprises: An application of statistical learning methods," Papers 1707.00757, arXiv.org.
  6. Limon Barua & Bo Zou & Yan Zhou & Yulin Liu, 2023. "Modeling household online shopping demand in the U.S.: a machine learning approach and comparative investigation between 2009 and 2017," Transportation, Springer, vol. 50(2), pages 437-476, April.
  7. Gerhard Tutz & Moritz Berger, 2016. "Item-focussed Trees for the Identification of Items in Differential Item Functioning," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 727-750, September.
  8. Montes, Ignacio & Miranda, Enrique & Montes, Susana, 2014. "Stochastic dominance with imprecise information," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 868-886.
  9. Wang, Hui & Mongiano, Gabriele & Fanchini, Davide & Titone, Patrizia & Tamborini, Luigi & Bregaglio, Simone, 2021. "Varietal susceptibility overcomes climate change effects on the future trends of rice blast disease in Northern Italy," Agricultural Systems, Elsevier, vol. 193(C).
  10. Enrico Biffis & Erik Chavez & Alexis Louaas & Pierre Picard, 2022. "Parametric insurance and technology adoption in developing countries," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 47(1), pages 7-44, March.
  11. Shu-Fu Kuo & Yu-Shan Shih, 2012. "Variable selection for functional density trees," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1387-1395, December.
  12. Daniel L. Chen & Markus Loecher, 2022. "Mood and the Malleability of Moral Reasoning: The Impact of Irrelevant Factors on Judicial Decisions," Working Papers hal-03864854, HAL.
  13. Rachel A. Oldroyd & Michelle A. Morris & Mark Birkin, 2021. "Predicting Food Safety Compliance for Informed Food Outlet Inspections: A Machine Learning Approach," IJERPH, MDPI, vol. 18(23), pages 1-20, November.
  14. Xiaomu Ye & Pengfei Ding & Dawei Jin & Chuanyue Zhou & Yi Li & Jin Zhang, 2023. "Intelligent Analysis of Construction Costs of Shield Tunneling in Complex Geological Conditions by Machine Learning Method," Mathematics, MDPI, vol. 11(6), pages 1-22, March.
  15. Hapfelmeier, A. & Ulm, K., 2014. "Variable selection by Random Forests using data with missing values," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 129-139.
  16. Saiedeh Haji-Maghsoudi & Azam Rastegari & Behshid Garrusi & Mohammad Reza Baneshi, 2018. "Addressing the problem of missing data in decision tree modeling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(3), pages 547-557, February.
  17. Ahlrichs, Jakob & Wenninger, Simon & Wiethe, Christian & Häckel, Björn, 2022. "Impact of socio-economic factors on local energetic retrofitting needs - A data analytics approach," Energy Policy, Elsevier, vol. 160(C).
  18. Jörg Kalbfuß & Reto Odermatt & Alois Stutzer, 2018. "Medical marijuana laws and mental health in the United States," CEP Discussion Papers dp1546, Centre for Economic Performance, LSE.
  19. Hapfelmeier, A. & Ulm, K., 2013. "A new variable selection approach using Random Forests," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 50-69.
  20. Maria Iannario & Marica Manisera & Domenico Piccolo & Paola Zuccolotto, 2012. "Sensory analysis in the food industry as a tool for marketing decisions," 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. 6(4), pages 303-321, December.
  21. Paola Zuccolotto, 2010. "Evaluating the impact of a grouping variable on Job Satisfaction drivers," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(2), pages 287-305, June.
  22. Timothy R Brick & Rachel E Koffer & Denis Gerstorf & Nilam Ram, 2018. "Feature Selection Methods for Optimal Design of Studies for Developmental Inquiry," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 73(1), pages 113-123.
  23. Qingrong Tan & Yan Cai & Fen Luo & Dongbo Tu, 2023. "Development of a High-Accuracy and Effective Online Calibration Method in CD-CAT Based on Gini Index," Journal of Educational and Behavioral Statistics, , vol. 48(1), pages 103-141, February.
  24. Wei-Yin Loh, 2014. "Fifty Years of Classification and Regression Trees," International Statistical Review, International Statistical Institute, vol. 82(3), pages 329-348, December.
  25. Achim Zeileis & Torsten Hothorn, 2013. "A toolbox of permutation tests for structural change," Statistical Papers, Springer, vol. 54(4), pages 931-954, November.
  26. Charles B. Perkins & J. Christina Wang, 2019. "How Magic a Bullet Is Machine Learning for Credit Analysis? An Exploration with FinTech Lending Data," Working Papers 19-16, Federal Reserve Bank of Boston.
  27. Carolin Strobl & Julia Kopf & Achim Zeileis, 2015. "Rasch Trees: A New Method for Detecting Differential Item Functioning in the Rasch Model," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 289-316, June.
  28. Yadegaridehkordi, Elaheh & Nilashi, Mehrbakhsh & Nizam Bin Md Nasir, Mohd Hairul & Momtazi, Saeedeh & Samad, Sarminah & Supriyanto, Eko & Ghabban, Fahad, 2021. "Customers segmentation in eco-friendly hotels using multi-criteria and machine learning techniques," Technology in Society, Elsevier, vol. 65(C).
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