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Unbiased split selection for classification trees based on the Gini Index


  • Strobl, Carolin
  • Boulesteix, Anne-Laure
  • Augustin, Thomas


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  • 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.
  • Handle: RePEc:eee:csdana:v:52:y:2007:i:1:p:483-501

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    References listed on IDEAS

    1. Boulesteix, Anne-Laure & Tutz, Gerhard, 2006. "Identification of interaction patterns and classification with applications to microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 783-802, February.
    2. 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.
    3. Shih, Yu-Shan & Tsai, Hsin-Wen, 2004. "Variable selection bias in regression trees with constant fits," Computational Statistics & Data Analysis, Elsevier, vol. 45(3), pages 595-607, April.
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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, 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.
    3. 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,
    4. 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.
    5. Montes, Ignacio & Miranda, Enrique & Montes, Susana, 2014. "Stochastic dominance with imprecise information," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 868-886.
    6. 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.
    7. 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.
    8. repec:taf:japsta:v:45:y:2018:i:3:p:547-557 is not listed on IDEAS
    9. 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.
    10. Hapfelmeier, A. & Ulm, K., 2013. "A new variable selection approach using Random Forests," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 50-69.
    11. 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.
    12. Wei-Yin Loh, 2014. "Fifty Years of Classification and Regression Trees," International Statistical Review, International Statistical Institute, vol. 82(3), pages 329-348, December.
    13. 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.

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