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

Dongrui Wu, Lawhern Vernon J., Stephen Gordon, Lance Brent J, Chin-Teng Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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.
Original languageEnglish
Title of host publication2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
PublisherIEEE
Pages724-729
Number of pages6
ISBN (Print)978-1-5090-1897-0
DOIs
StatePublished - 2016

Publication series

NameIEEE International Conference on Systems Man and Cybernetics Conference Proceedings
ISSN (Print)1062-922X

Keywords

  • Brain-computer interface
  • classification
  • EEG
  • ensemble learning
  • maximum likelihood estimator

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    Wu, D., Vernon J., L., Gordon, S., Brent J, L., & Lin, C-T. (2016). Agreement Rate Initialized Maximum Likelihood Estimator for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface. In 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) (pp. 724-729). (IEEE International Conference on Systems Man and Cybernetics Conference Proceedings). IEEE. https://doi.org/10.1109/SMC.2016.7844327