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The use of classification trees for bioinformatics

Authors

  • Xiang Chen,

    1. Department of Epidemiology and Public Health, School of Medicine, Yale University, New Haven, CT, USA
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  • Minghui Wang,

    1. Department of Epidemiology and Public Health, School of Medicine, Yale University, New Haven, CT, USA
    2. University of Science and Technology, Hefei, Anhui, China
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  • Heping Zhang

    Corresponding author
    1. Department of Epidemiology and Public Health, School of Medicine, Yale University, New Haven, CT, USA
    • Department of Epidemiology and Public Health, School of Medicine, Yale University, New Haven, CT, USA
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Abstract

Classification trees are nonparametric statistical learning methods that incorporate feature selection and interactions, possess intuitive interpretability, are efficient, and have high prediction accuracy when used in ensembles. This paper provides a brief introduction to the classification tree-based methods, a review of the recent developments, and a survey of the applications in bioinformatics and statistical genetics. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 55-63 DOI: 10.1002/widm.14

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