Online Semi-supervised Ensemble Updates for fMRI Data

  • Catrin O. Plumpton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7081)


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.


Semi-supervised learning random subspace ensemble fMRI 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Catrin O. Plumpton
    • 1
  1. 1.School of Computer ScienceBangor UniversityUnited Kingdom

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