Online adaptive policies for ensemble classifiers

Authors:
Christos Dimitrakakis;Samy Bengio
Affiliations:
IDIAP, P.O. Box 592, CH-1920 Martigny, Switzerland;IDIAP, P.O. Box 592, CH-1920 Martigny, Switzerland
Venue:
Neurocomputing
Year:
2005

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Abstract

Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper, we attempt to train and combine the base classifiers using an adaptive policy. This policy is learnt through a Q-learning inspired technique. Its effectiveness for an essentially supervised task is demonstrated by experimental results on several UCI benchmark databases.