標題: Agreement Rate Initialized Maximum Likelihood Estimator for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface
作者: Wu, Dongrui
Lawhern, Vernon J.
Gordon, Stephen
Lance, Brent J.
Lin, Chin-Teng
腦科學研究中心
Brain Research Center
關鍵字: Brain-computer interface;classification;EEG;ensemble learning;maximum likelihood estimator
公開日期: 1-一月-2016
摘要: 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
期刊: 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
起始頁: 724
結束頁: 729
顯示於類別:會議論文