Recurrent Boosting for Classification of Natural and Synthetic Time-Series Data

  • Robert D. Vincent
  • Joelle Pineau
  • Philip de Guzman
  • Massimo Avoli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4509)


Boosted ensemble classifiers have a demonstrated ability to discover regularities in large, poorly modeled datasets. In this paper we present an application of multi-hypothesis AdaBoost to detect epileptiform activity from electrophysiological recordings. While existing boosting methods do not account automatically for the sequence information that is available when analyzing time-series data, we present a recurrent extension to AdaBoost, and show that it improves classification accuracy in our application domain.


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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Robert D. Vincent
    • 1
  • Joelle Pineau
    • 1
  • Philip de Guzman
    • 2
  • Massimo Avoli
    • 2
  1. 1.School of Computer Science, McGill University, Montreal, QuebecCanada
  2. 2.Montreal Neurological Institute, McGill University, Montreal, QuebecCanada

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