PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bayesian Classifier Combination
Zoubin Ghahramani and Hyun-Chul Kim
(2003) Technical Report. University College London, London, UK.

Abstract

Bayesian model averaging linearly mixes the probabilistic predictions of multiple models, each weighted by its posterior probability. This is the coherent Bayesian way of combining multiple models only under very restrictive assumptions, which we outline. We explore a general framework for Bayesian model combination (which differs from model averaging) in the context of classification. This framework explicitly models the relationship between each model's output and the unknown true label. The framework does not require that the models be probabilistic (they can even be human assessors), that they share prior information or receive the same training data, or that they be independent in their errors. Finally, the Bayesian combiner does not need to believe any of the models is in fact correct. We test several variants of this classifier combination procedure starting from a classic statistical model proposed by [1] and using MCMC to add more complex but important features to the model. Comparisons on several datasets to simpler methods like majority voting show that the Bayesian methods not only perform well but result in interpretable diagnostics on the data points and the models.

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EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:806
Deposited By:Zoubin Ghahramani
Deposited On:30 December 2004