Mañana, JUEVES, 24 DE ABRIL, el sistema se apagará debido a tareas habituales de mantenimiento a partir de las 9 de la mañana. Lamentamos las molestias.
Selection of decision stumps in bagging ensembles
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Springer Berlin HeidelbergDate
2007Citation
10.1007/978-3-540-74690-4_33
Artificial Neural Networks – ICANN 2007: 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part I. Lecture Notes in Computer Science, Volumen 4668. Springer, 2007. 319-328.
ISSN
0302-9743 (print); 1611-3349 (online)ISBN
978-3-540-74689-8 (print); 978-3-540-74690-4 (online)DOI
10.1007/978-3-540-74690-4_33Funded by
This work has been supported by Consejería de Educación de la Comunidad Autónoma de Madrid, European Social Fund, and the Dirección General de Investigación, grant TIN2004-07676-C02-02Editor's Version
http://dx.doi.org/10.1007/978-3-540-74690-4_33Subjects
Computation by Abstract Devices; Pattern Recognition; Information Systems Applications; Database Management; Neurosciences; InformáticaNote
Proceedings of 17th International Conference ICANN, Porto, Portugal, September 9-13, 2007.The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-74690-4_33
Rights
© Springer-Verlag Berlin Heidelberg 2007Abstract
This article presents a comprehensive study of different ensemble pruning techniques applied to a bagging ensemble composed of decision stumps. Six different ensemble pruning methods are tested. Four of these are greedy strategies based on first reordering the elements of the ensemble according to some rule that takes into account the complementarity of the predictors with respect to the classification task. Subensembles of increasing size are then constructed by incorporating the ordered classifiers one by one. A halting criterion stops the aggregation process before the complete original ensemble is recovered. The other two approaches are selection techniques that attempt to identify optimal subensembles using either genetic algorithms or semidefinite programming. Experiments performed on 24 benchmark classification tasks show that the selection of a small subset (≈ 10 − 15%) of the original pool of stumps generated with bagging can significantly increase the accuracy and reduce the complexity of the ensemble.
Files in this item
Google Scholar:Martínez Muñoz, Gonzalo
-
Hernández Lobato, Daniel
-
Suárez González, Alberto
This item appears in the following Collection(s)
Related items
Showing items related by title, author, creator and subject.