Bundling classifiers by bagging trees

Authors:
Torsten Hothorn;Berthold Lausen
Affiliations:
Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Waldstraíe 6, D-91054 Erlangen, Germany;Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Waldstraíe 6, D-91054 Erlangen, Germany
Venue:
Computational Statistics & Data Analysis
Year:
2005

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Abstract

The quest of selecting the best classifier for a discriminant analysis problem is often rather difficult. A combination of different types of classifiers promises to lead to improved predictive models compared to selecting one of the competitors. An additional learning sample, for example the out-of-bag sample, is used for the training of arbitrary classifiers. Classification trees are employed to bundle their predictions for the bootstrap sample. Consequently, a combined classifier is developed. Benchmark experiments show that the combined classifier is superior to any of the single classifiers in many applications.