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WIREs Data Mining Knowl Discov

The use of classification trees for bioinformatics

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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

Figure 1.

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].

[ Normal View 18K | Magnified View 43K ]
Figure 2.

Impurity functions.

[ Normal View 6K | Magnified View 14K ]

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Algorithmic Development > Statistics
Technologies > Classification
Technologies > Machine Learning
Algorithmic Development > Hierarchies and Trees
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