An ensemble of classifiers approach with multiple sources of information

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“An ensemble of classifiers approach with multiple sources of information” by R. Santana, C. Bielza, and P. Larrañaga. In Proceedings of ICANN/PASCAL2 Challenge: MEG Mind Reading, (A. Klami, ed.), 2011, pp. 25-30.

Abstract

This paper describes the main characteristics of our approach to the ICANN-2011 Mind reading from MEG - PASCAL Challenge. The distinguished features of our method are: 1) The use of different sources of information as input to the classifiers. We simultaneously use information coming from raw data, channels correlations, mutual information between channels, and channel interactions graphs as features for the classifiers. 2) The use of ensemble of classifiers based on regularized multi-logistic regression, regression trees, and an affinity propagation based classifier.

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BibTeX entry:

@inproceedings{Santana_et_al:2011f,
   author = {R. Santana and C. Bielza and P. Larra{\~n}aga},
   editor = {A. Klami},
   title = {An ensemble of classifiers approach with multiple sources of
	information},
   booktitle = {Proceedings of ICANN/PASCAL2 Challenge: MEG Mind Reading},
   series = {Aalto University Publication series {SCIENCE + TECHNOLOGY}},
   pages = {25--30},
   publisher = {Aalto University},
   year = {2011}
}

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