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Classification Trees With Unbiased Multiway Splits

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  • Kim H.
  • Loh W.Y.

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  • 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.
    • Handle: RePEc:bes:jnlasa:v:96:y:2001:m:june:p:589-604
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Shih, Y. -S., 2004. "A note on split selection bias in classification trees," Computational Statistics & Data Analysis, Elsevier, vol. 45(3), pages 457-466, April.
    4. Andriyashin, Anton, 2008. "Stock picking via nonsymmetrically pruned binary decision trees," SFB 649 Discussion Papers 2008-035, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    5. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    6. repec:hum:wpaper:sfb649dp2008-035 is not listed on IDEAS
    7. Christophe Dutang & Quentin Guibert, 2021. "An explicit split point procedure in model-based trees allowing for a quick fitting of GLM trees and GLM forests," Post-Print hal-03448250, HAL.
    8. Hothorn, Torsten & Lausen, Berthold, 2005. "Bundling classifiers by bagging trees," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1068-1078, June.
    9. Lee, Tzu-Haw & Shih, Yu-Shan, 2006. "Unbiased variable selection for classification trees with multivariate responses," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 659-667, November.
    10. Ollech, Daniel & Webel, Karsten, 2020. "A random forest-based approach to identifying the most informative seasonality tests," Discussion Papers 55/2020, Deutsche Bundesbank.
    11. Alvarez-Iglesias, Alberto & Hinde, John & Ferguson, John & Newell, John, 2017. "An alternative pruning based approach to unbiased recursive partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 90-102.
    12. Gray, J. Brian & Fan, Guangzhe, 2008. "Classification tree analysis using TARGET," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1362-1372, January.
    13. Noh, Hyun Gon & Song, Moon Sup & Park, Sung Hyun, 2004. "An unbiased method for constructing multilabel classification trees," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 149-164, August.
    14. Dimitris Bertsimas & Margrét V. Bjarnadóttir & Michael A. Kane & J. Christian Kryder & Rudra Pandey & Santosh Vempala & Grant Wang, 2008. "Algorithmic Prediction of Health-Care Costs," Operations Research, INFORMS, vol. 56(6), pages 1382-1392, December.
    15. Emilio Carrizosa & Cristina Molero-Río & Dolores Romero Morales, 2021. "Mathematical optimization in classification and regression trees," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 5-33, April.
    16. 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.
    17. Cédric Beaulac & Jeffrey S. Rosenthal, 2019. "Predicting University Students’ Academic Success and Major Using Random Forests," Research in Higher Education, Springer;Association for Institutional Research, vol. 60(7), pages 1048-1064, November.

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