Class-switching neural network ensembles
EntityUAM. Departamento de Ingeniería Informática
10.1016/j.neucom.2007.11.041Neurocomputing 71.13-15 (2008): 2521 – 2528
ISSN0925-2312 (print); 1872-8286 (online)
Funded byThe authors acknowledge nancial support from the Spanish Dirección General de Investigación, project TIN2004-07676-C02-02.
SubjectsBagging; Boosting; Class-switching; Decision trees; Ensembles of classifiers; Neural networks; Informática
NoteThis is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing 71.13-15 (2008): 2521 – 2528. DOI: 10.1016/j.neucom.2007.11.041
Special papers: Artificial Neural Networks (ICANN 2006)
Rights© 2008 Elsevier B.V. All rights reserved
Esta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
This article investigates the properties of class-switching ensembles composed of neural networks and compares them to class-switching ensembles of decision trees and to standard ensemble learning methods, such as bagging and boosting. In a class-switching ensemble, each learner is constructed using a modi ed version of the training data. This modi cation consists in switching the class labels of a fraction of training examples that are selected at random from the original training set. Experiments on 20 benchmark classi cation problems, including real-world and synthetic data, show that class-switching ensembles composed of neural networks can obtain signi cant improvements in the generalization accuracy over single neural networks and bagging and boosting ensembles. Furthermore, it is possible to build mediumsized ensembles ( 200 networks) whose classi cation performance is comparable to larger class-switching ensembles ( 1000 learners) of unpruned decision trees.
Google Scholar:Martínez Muñoz, Gonzalo - Sánchez-Martínez, Aitor - Hernández Lobato, Daniel - Suárez González, Alberto
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