IDEAS home Printed from https://ideas.repec.org/r/eee/csdana/v52y2007i1p483-501.html

Unbiased split selection for classification trees based on the Gini Index

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. 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).
  2. 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.
  3. 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.
  4. Gongxun Lu & Zhiyuan Han & Lei Shi & Zhilong Wang & Mengtian Zhang & Xinru Wu & Zhihong Piao & Xiao Xiao & Shengyu Tao & Jianwei Nai & Zhijin Ju & Xuan Zhang & Yanqiang Han & Karl Luigi Loza Vidaurre , 2025. "Decoding single-crystal lithium growth through solid electrolyte interphase omics," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
  5. 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.
  6. 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.
  7. Kalbfuss, Jörg & Odermatt, Reto & Stutzer, Alois, 2024. "Medical marijuana laws and mental health in the United States," Health Economics, Policy and Law, Cambridge University Press, vol. 19(3), pages 307-322, July.
  8. 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.
  9. 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.
  10. 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.
  11. David Podgorelec & Borut Žalik & Domen Mongus & Dino Vlahek, 2024. "A New Alternating Suboptimal Dynamic Programming Algorithm with Applications for Feature Selection," Mathematics, MDPI, vol. 12(13), pages 1-22, June.
  12. 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).
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. Montes, Ignacio & Miranda, Enrique & Montes, Susana, 2014. "Stochastic dominance with imprecise information," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 868-886.
  18. Peipei Wang & Melissa D. Lehti-Shiu & Serena Lotreck & Kenia Segura Abá & Patrick J. Krysan & Shin-Han Shiu, 2024. "Prediction of plant complex traits via integration of multi-omics data," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
  19. 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).
  20. 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.
  21. 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.
  22. Julie Josse & Jacob M. Chen & Nicolas Prost & Gaël Varoquaux & Erwan Scornet, 2024. "On the consistency of supervised learning with missing values," Statistical Papers, Springer, vol. 65(9), pages 5447-5479, December.
  23. 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.
  24. Nur Şahver Uslu & Ali Hakan Büyüklü, 2024. "The Dynamics of the Profit Margin in a Component Maintenance, Repair, and Overhaul (MRO) within the Aviation Industry: An Analytical Approach Using Gradient Boosting, Variable Clustering, and the Gini Index," Sustainability, MDPI, vol. 16(15), pages 1-31, July.
  25. 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.
  26. 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).
  27. Hapfelmeier, A. & Ulm, K., 2013. "A new variable selection approach using Random Forests," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 50-69.
  28. 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.
  29. 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.
  30. 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.
  31. Wei-Yin Loh, 2014. "Fifty Years of Classification and Regression Trees," International Statistical Review, International Statistical Institute, vol. 82(3), pages 329-348, December.
  32. Achim Zeileis & Torsten Hothorn, 2013. "A toolbox of permutation tests for structural change," Statistical Papers, Springer, vol. 54(4), pages 931-954, November.
  33. 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.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.