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

Authors:
Robert D. Vincent;Joelle Pineau;Philip Guzman;Massimo Avoli
Affiliations:
School of Computer Science, McGill University, Montreal, Quebec, Canada;School of Computer Science, McGill University, Montreal, Quebec, Canada;Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada;Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
Venue:
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Year:
2007

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Abstract

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.