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Ensemble learning is a powerful approach to construct a strong learner frommultiple base learners. The most popular way to aggregate an ensemble ofclassifiers is majority voting, which assigns a sample to the class that mostbase classifiers vote for. However, improved performance can be obtained byassigning weights to the base classifiers according to their accuracy. Thispaper proposes an agreement rate initialized maximum likelihood estimator(ARIMLE) to optimally fuse the base classifiers. ARIMLE first uses a simplifiedagreement rate method to estimate the classification accuracy of each baseclassifier from the unlabeled samples, then employs the accuracies toinitialize a maximum likelihood estimator (MLE), and finally uses theexpectation-maximization algorithm to refine the MLE. Extensive experiments onvisually evoked potential classification in a brain-computer interfaceapplication show that ARIMLE outperforms majority voting, and also achievesbetter or comparable performance with several other state-of-the-art classifiercombination approaches.

Agreement Rate Initialized Maximum Likelihood Estimator for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface