Agreement Rate Initialized Maximum Likelihood Estimator for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface
This work addresses the challenge of enhancing ensemble classifier aggregation for brain-computer interface systems, representing an incremental improvement over existing methods.
The paper tackles the problem of optimally aggregating ensemble classifiers by proposing an agreement rate initialized maximum likelihood estimator (ARIMLE), which estimates base classifier accuracies from unlabeled data and refines them via expectation-maximization, resulting in improved performance over majority voting and competitive results with other state-of-the-art methods in brain-computer interface applications.
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.