Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
Regularized logistic regression and multi-objective variable selection for classifying MEG data
Year:2012
Research Areas
  • Artificial intelligence
Information
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.
International
Si
JCR
Si
Title
Biological Cybernetics
ISBN
0340-1200
Impact factor JCR
1,586
Impact info
Volume
106
Journal number
6-7
From page
389
To page
405
Month
SIN MES
Ranking
Participants
  • Autor: Roberto Santana Hermida (UPM)
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
  • Departamento: Inteligencia Artificial
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Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
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