title: Regularized logistic regression and multiobjective variable selection for classifying MEG data.
abstract: This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori.
authors.affiliation: Intelligent Systems Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain. roberto.santana@ehu.es
authors.forename: Roberto
authors.initials: R
authors.lastname: Santana
authors.forename: Concha
authors.initials: C
authors.lastname: Bielza
authors.forename: Pedro
authors.initials: P
authors.lastname: Larrañaga
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence / Q000706:statistics & numerical data; D062207:Brain-Computer Interfaces / Q000706:statistics & numerical data*; D003491:Cybernetics; D003627:Data Interpretation, Statistical; D006801:Humans; D016015:Logistic Models; D015225:Magnetoencephalography / Q000706:statistics & numerical data*; D015233:Models, Statistical