AbstractBagging has been found to be successful in increasing the predictive performance of unstable classifiers. Bagging draws bootstrap samples from the training sample, applies the classifier to each bootstrap sample, and then averages over all obtained classification rules. The idea of trimmed bagging is to exclude the bootstrapped classification rules that yield the highest error rates, as estimated by the out-of-bag error rate, and to aggregate over the remaining ones. In this note we explore the potential benefits of trimmed bagging. On the basis of numerical experiments, we conclude that trimmed bagging performs comparably to standard bagging when applied to unstable classifiers as decision trees, but yields better results when applied to more stable base classifiers, like support vector machines.
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Bibliographic InfoPaper provided by Katholieke Universiteit Leuven in its series Open Access publications from Katholieke Universiteit Leuven with number urn:hdl:123456789/201706.
Date of creation: 2007
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Publication status: Published in Computational statistics & data analysis (2007) v.52, p.362-368
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- Nerini, David & Ghattas, Badih, 2007. "Classifying densities using functional regression trees: Applications in oceanology," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4984-4993, June.
- De Bock, Koen W & Coussement, Kristof & Van den Poel, Dirk, 2010.
"Ensemble classification based on generalized additive models,"
2010/02, Hogeschool-Universiteit Brussel, Faculteit Economie en Management.
- De Bock, Koen W. & Coussement, Kristof & Van den Poel, Dirk, 2010. "Ensemble classification based on generalized additive models," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1535-1546, June.
- K. W. De Bock & K. Coussement & D. Van Den Poel, 2009. "Ensemble classification based on generalized additive models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/625, Ghent University, Faculty of Economics and Business Administration.
- Rokach, Lior, 2009. "Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4046-4072, October.
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