Information theoretic combination of classifiers with application to AdaBoost

Authors:
Julien Meynet;Jean-Philippe Thiran
Affiliations:
Signal Processing Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland;Signal Processing Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Venue:
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Year:
2007

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Abstract

Combining several classifiers has proved to be an efficient machine learning technique. We propose a new measure of the goodness of an ensemble of classifiers in an information theoretic framework. It measures a trade-off between diversty and individual classifier accuracy. This technique can be directly used for the selection of an ensemble in a pool of classifiers. We also propose a variant of AdaBoost for directly training the classifiers by taking into account this new information theoretic measure.