An analysis of ensemble pruning techniques based on ordered aggregation
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
IEEEDate
2009-02Citation
10.1109/TPAMI.2008.78
IEEE Transactions on Pattern Analysis and Machine Intelligence 31.2 (2009): 245 – 259
ISSN
0162-8828 (print); 1939-3539 (online)DOI
10.1109/TPAMI.2008.78Funded by
The authors acknowledge support form the Spanish Ministerio de Educación y Ciencia under Project TIN2007-66862-C02-02Editor's Version
http://dx.doi.org/10.1109/TPAMI.2008.78Subjects
Bagging; Decision trees; Ensemble Pruning; Ensemble Selection; Ensembles of classifiers; Ordered Aggregation; InformáticaNote
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. G. Martínez-Muñoz, D. Hernández-Lobato and A. Suárez, "An analysis of ensemble pruning techniques based on ordered aggregation", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 245-249, February 2009Rights
© 2009 IEEEAbstract
Several pruning strategies that can be used to reduce the size and increase the accuracy of bagging ensembles are analyzed. These heuristics select subsets of complementary classifiers that, when combined, can perform better than the whole ensemble. The pruning methods investigated are based on modifying the order of aggregation of classifiers in the ensemble. In the original bagging algorithm, the order of aggregation is left unspecified. When this order is random, the generalization error typically decreases as the number of classifiers in the ensemble increases. If an appropriate ordering for the aggregation process is devised, the generalization error reaches a minimum at intermediate numbers of classifiers. This minimum lies below the asymptotic error of bagging. Pruned ensembles are obtained by retaining a fraction of the classifiers in the ordered ensemble. The performance of these pruned ensembles is evaluated in several benchmark classification tasks under different training conditions. The results of this empirical investigation show that ordered aggregation can be used for the efficient generation of pruned ensembles that are competitive, in terms of performance and robustness of classification, with computationally more costly methods that directly select optimal or near-optimal subensembles.
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Google Scholar:Martínez Muñoz, Gonzalo
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Hernández Lobato, Daniel
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Suárez González, Alberto
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