Balanced boosting with parallel perceptrons
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Springer Berlin HeidelbergDate
2005Citation
10.1007/11494669_26
Computational Intelligence and Bioinspired Systems: 8th International Work-Conference on Artificial Neural Networks, IWANN 2005, Vilanova i la Geltrú, Barcelona, Spain, June 8-10, 2005. Proceedings. Lecture Notes in Computer Science, Volumen 3512. Springer 2005. 208-216.
ISSN
0302-9743 (print); 1611-3349 (online)ISBN
978-3-540-26208-4 (print); 978-3-540-32106-4 (online)DOI
10.1007/11494669_26Funded by
With partial support of Spain’s CICyT, TIC 01–572.Editor's Version
http://dx.doi.org/10.1007/11494669_26Subjects
Computation by Abstract Devices; Algorithm Analysis and Problem Complexity; Image Processing and Computer Vision; Pattern Recognition; Evolutionary Biology; InformáticaNote
The final publication is available at Springer via http://dx.doi.org/10.1007/11494669_26Proceedings of 8th International Work-Conference on Artificial Neural Networks, IWANN 2005, Vilanova i la Geltrú, Barcelona, Spain, June 8-10, 2005.
Rights
© Springer-Verlag Berlin Heidelberg 2005Abstract
Boosting constructs a weighted classifier out of possibly weak learners by successively concentrating on those patterns harder to classify. While giving excellent results in many problems, its performance can deteriorate in the presence of patterns with incorrect labels. In this work we shall use parallel perceptrons (PP), a novel approach to the classical committee machines, to detect whether a pattern’s label may not be correct and also whether it is redundant in the sense of being well represented in the training sample by many other similar patterns. Among other things, PP allow to naturally define margins for hidden unit activations, that we shall use to define the above pattern types. This pattern type classification allows a more nuanced approach to boosting. In particular, the procedure we shall propose, balanced boosting, uses it to modify boosting distribution updates. As we shall illustrate numerically, balanced boosting gives very good results on relatively hard classification problems, particularly in some that present a marked imbalance between class sizes.
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Google Scholar:Cantador Gutiérrez, Iván
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Dorronsoro Ibero, José Ramón
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