  Ensemble learning is a powerful approach to construct a strong learner from
multiple base learners. The most popular way to aggregate an ensemble of
classifiers is majority voting, which assigns a sample to the class that most
base classifiers vote for. However, improved performance can be obtained by
assigning weights to the base classifiers according to their accuracy. This
paper proposes an agreement rate initialized maximum likelihood estimator
(ARIMLE) to optimally fuse the base classifiers. ARIMLE first uses a simplified
agreement rate method to estimate the classification accuracy of each base
classifier from the unlabeled samples, then employs the accuracies to
initialize a maximum likelihood estimator (MLE), and finally uses the
expectation-maximization algorithm to refine the MLE. Extensive experiments on
visually evoked potential classification in a brain-computer interface
application show that ARIMLE outperforms majority voting, and also achieves
better or comparable performance with several other state-of-the-art classifier
combination approaches.
