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Título

Robust Bayesian Linear Classifier Ensembles

Autor Cerquides, Jesus; Lopez de Mantaras, Ramon
Palabras clave Artificial Intelligence
Bayesian model averaging
Averaged One Dependence
Fecha de publicación 2005
EditorSpringer
Citación Machine Learning: ECML 2005. 16th European Conference on Machine Learning, Porto, Portugal, October 3-7, 2005. Proceedings. Lecture Notes in Artificial Intelligence, Vol. 3720, p.p.: 72-83, Springer Berlin / Heidelberg, 2005.
ResumenEnsemble classifiers combine the classification results of several classifiers. Simple ensemble methods such as uniform averaging over a set of models usually provide an improvement over selecting the single best model. Usually probabilistic classifiers restrict the set of possible models that can be learnt in order to lower computational complexity costs. In these restricted spaces, where incorrect modelling assumptions are possibly made, uniform averaging sometimes performs even better than bayesian model averaging. Linear mixtures over sets of models provide an space that includes uniform averaging as a particular case. We develop two algorithms for learning maximum a posteriori weights for linear mixtures, based on expectation maximization and on constrained optimization. We provide a nontrivial example of the utility of these two algorithms by applying them for one dependence estimators.We develop the conjugate distribution for one dependence estimators and empirically show that uniform averaging is clearly superior to BMA for this family of models. After that we empirically show that the maximum a posteriori linear mixture weights improve accuracy significantly over uniform aggregation.
Descripción The original publication is available at http://www.springerlink.com
URI http://hdl.handle.net/10261/3221
DOI10.1007/11564096_12
ISBN 978-3-540-29243-2
ISSN0302-9743
Aparece en las colecciones: (IIIA) Comunicaciones congresos
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