Title: Agreement Rate Initialized Maximum Likelihood Estimator for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface
Authors: Wu, Dongrui
Lawhern, Vernon J.
Gordon, Stephen
Lance, Brent J.
Lin, Chin-Teng
Brain Research Center
Keywords: Brain-computer interface;classification;EEG;ensemble learning;maximum likelihood estimator
Issue Date: 1-Jan-2016
Abstract: 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.
URI: http://hdl.handle.net/11536/146597
ISSN: 1062-922X
Begin Page: 724
End Page: 729
Appears in Collections:Conferences Paper