Research Publications in journals:
Regularized logistic regression and multi-objective variable selection for classifying MEG data
Year:2012
Research Areas
- Artificial intelligence
Information
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Abstract
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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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International
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Si |
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JCR
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Si |
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Title
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Biological Cybernetics |
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ISBN
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0340-1200 |
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Impact factor JCR
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1,586 |
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Impact info
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Volume
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106 |
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Journal number
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6-7 |
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From page
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389 |
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To page
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405 |
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Month
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SIN MES |
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Ranking
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Open Access publication
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