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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Online Semi-supervised Ensemble Updates for fMRI Data
verfasst von : Catrin O. Plumpton
Erschienen in: Partially Supervised Learning
Verlag: Springer Berlin Heidelberg
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Advances in Eelectroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have opened up the possibility for real time data classification. A small amount of labelled training data is usually available, followed by a large stream of unlabelled data. Noise and possible concept drift pose a further challenge. A fixed pre-trained classifier may not always work. One solution is to update the classifier in real-time. Since true labels are not available, the classifier is updated using the predicted label, a method called naive labelling. We propose to use classifier ensembles in order to counteract the adverse effect of ‘run-away’ classifiers, associated with naive labelling. A new ensemble method for naive labelling is proposed. The label taken to update each member-classifier is the ensemble prediction. We use an fMRI dataset to demonstrate the advantage of the proposed method over the fixed classifier and the single classifier updated through naive labelling.