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Online Semi-supervised Ensemble Updates for fMRI Data

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Partially Supervised Learning (PSL 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7081))

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Abstract

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.

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Authors and Affiliations

  1. School of Computer Science, Bangor University, Dean Street, LL57 1UT, United Kingdom

    Catrin O. Plumpton

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  1. Catrin O. Plumpton
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Friedhelm Schwenker Edmondo Trentin

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Plumpton, C.O. (2012). Online Semi-supervised Ensemble Updates for fMRI Data. In: Schwenker, F., Trentin, E. (eds) Partially Supervised Learning. PSL 2011. Lecture Notes in Computer Science(), vol 7081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28258-4_2

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  • DOI: https://doi.org/10.1007/978-3-642-28258-4_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28257-7

  • Online ISBN: 978-3-642-28258-4

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