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Selecting the best splits for classification trees with categorical variables

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  • Shih, Yu-Shan

Abstract

Based on a family of splitting criteria for classification trees, methods of selecting the best categorical splits are studied. They are shown to be very useful in reducing the computational complexity of the exhaustive search method.

Suggested Citation

  • Shih, Yu-Shan, 2001. "Selecting the best splits for classification trees with categorical variables," Statistics & Probability Letters, Elsevier, vol. 54(4), pages 341-345, October.
    • Handle: RePEc:eee:stapro:v:54:y:2001:i:4:p:341-345
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    Cited by:

    1. Antonella Plaia & Mariangela Sciandra, 2019. "Weighted distance-based trees for ranking 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. 13(2), pages 427-444, June.
    2. Mariangela Sciandra & Antonella Plaia & Vincenza Capursi, 2017. "Classification trees for multivariate ordinal response: an application to Student Evaluation Teaching," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 641-655, March.

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