Hothorn, T and Lausen, B (2005) 'Bundling classifiers by bagging trees.' Computational Statistics & Data Analysis, 49 (4). pp. 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.

Item Type:	Article
Uncontrolled Keywords:	Bagging; Ensemble-methods; Method selection; Error rate estimation
Subjects:	H Social Sciences > HA Statistics
Divisions:	Faculty of Science and Health Faculty of Science and Health > Mathematical Sciences, Department of
SWORD Depositor:	Elements
Depositing User:	Elements
Date Deposited:	31 May 2012 12:58
Last Modified:	06 Jan 2022 13:24
URI:	http://repository.essex.ac.uk/id/eprint/2460

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