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.
Out of bootstrap estimation of generalization error curves in bagging ensembles
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Springer Berlin HeidelbergDate
2004Citation
10.1007/978-3-540-77226-2_6
Intelligent Data Engineering and Automated Learning - IDEAL 2007: 8th International Conference, Birmingham, UK, December 16-19, 2007. Proceedings. Lecture Notes in Computer Science, Volumen 4881. Springer, 2007. 47-56.
ISSN
0302-9743 (print); 1611-3349 (online)ISBN
978-3-540-77225-5 (print); 978-3-540-77226-2 (online)DOI
10.1007/978-3-540-77226-2_6Editor's Version
http://dx.doi.org/10.1007/978-3-540-77226-2_6Subjects
Database Management; Algorithm Analysis and Problem Complexity; Information Systems Applications; Data Mining and Knowledge Discovery; InformáticaNote
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-77226-2_6Proceedings of 8th International Conference IDEAL, Birmingham, UK, December 16-19, 2007.
Rights
© Springer-Verlag Berlin Heidelberg 2007Abstract
The dependence of the classification error on the size of a bagging ensemble can be modeled within the framework of Monte Carlo theory for ensemble learning. These error curves are parametrized in terms of the probability that a given instance is misclassified by one of the predictors in the ensemble. Out of bootstrap estimates of these probabilities can be used to model generalization error curves using only information from the training data. Since these estimates are obtained using a finite number of hypotheses, they exhibit fluctuations. This implies that the modeled curves are biased and tend to overestimate the true generalization error. This bias becomes negligible as the number of hypotheses used in the estimator becomes sufficiently large. Experiments are carried out to analyze the consistency of the proposed estimator.
Files in this item
Google Scholar:Hernández Lobato, Daniel
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Martínez Muñoz, Gonzalo
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Suárez González, Alberto
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