Artículos en revistas:
Regularized logistic regression and multi-objective variable selection for classifying MEG data
Año:2012
Áreas de investigación
- Inteligencia artificial
Datos
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Descripción
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| 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. | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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Biological Cybernetics |
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ISSN
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0340-1200 |
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Factor de impacto JCR
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1,586 |
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Información de impacto
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Volumen
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106 |
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DOI
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Número de revista
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6-7 |
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Desde la página
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389 |
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Hasta la página
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405 |
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Mes
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SIN MES |
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Ranking
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Ver publicación en Archivo digital upm
Esta actividad pertenece a memorias de investigación
Participantes
- Autor: Roberto Santana Hermida UPM
- Autor: Maria Concepcion Bielza Lozoya UPM
- Autor: Pedro Maria Larrañaga Mugica UPM
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
- Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
- Departamento: Inteligencia Artificial