The use of classification trees for bioinformatics

Advanced Review

Heping Zhang, Minghui Wang, Xiang Chen

Published Online: Jan 06 2011

DOI: 10.1002/widm.14

Full article on Wiley Online Library: HTML PDF

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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 This article is categorized under: Algorithmic Development > Hierarchies and Trees Algorithmic Development > Statistics Technologies > Classification Technologies > Machine Learning

The annual number of publications related to classification tree or random forest in PUBMED between 1990 and 2009. The example query used for 1990 is: ‘classification tree’ [All Fields] or ‘decision tree’ [All Fields] or ‘random forest’ [All Fields] and ‘1990’ [Enter Date].

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Impurity functions.

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