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Double-Bagging: Combining Classifiers by Bootstrap Aggregation

Hothorn, T and Lausen, B (2003) 'Double-Bagging: Combining Classifiers by Bootstrap Aggregation.' Pattern Recognition, 36 (6). pp. 1303-1309. ISSN 0031-3203

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The combination of classifiers leads to substantial reduction of misclassification error in a wide range of applications and benchmark problems. We suggest using an out-of-bag sample for combining different classifiers. In our setup, a linear discriminant analysis is performed using the observations in the out-of-bag sample, and the corresponding discriminant variables computed for the observations in the bootstrap sample are used as additional predictors for a classification tree. Two classifiers are combined and therefore method and variable selection bias is no problem for the corresponding estimate of misclassification error, the need of an additional test sample disappears. Moreover, the procedure performs comparable to the best classifiers used in a number of artificial examples and applications.

Item Type: Article
Uncontrolled Keywords: Bagging; Classification; Discriminant analysis; Method selection bias; 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: 04 Jul 2012 21:44
Last Modified: 06 Jan 2022 13:24

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