Hothorn, T and Lausen, B (2005) 'Bundling classifiers by bagging trees.' Computational Statistics and Data Analysis, 49 (4). 1068 - 1078. ISSN 0167-9473

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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. © 2004 Elsevier B.V. All rights reserved.

Item Type:	Article
Subjects:	H Social Sciences > HA Statistics
Divisions:	Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User:	Berthold Lausen
Date Deposited:	31 May 2012 12:58
Last Modified:	30 Jan 2019 16:21
URI:	http://repository.essex.ac.uk/id/eprint/2460

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