Regularized logistic regression and multi-objective variable selection for classifying MEG data

Santana, Roberto and Larrañaga Múgica, Pedro and Bielza Lozoya, Maria Concepcion (2012). Regularized logistic regression and multi-objective variable selection for classifying MEG data. "Biological Cybernetics", v. 106 (n. 6-7); pp. 389-405. ISSN 0340-1200. https://doi.org/10.1007/s00422-012-0506-6.

Description

Title: Regularized logistic regression and multi-objective variable selection for classifying MEG data
Author/s:
  • Santana, Roberto
  • Larrañaga Múgica, Pedro
  • Bielza Lozoya, Maria Concepcion
Item Type: Article
Título de Revista/Publicación: Biological Cybernetics
Date: September 2012
ISSN: 0340-1200
Volume: 106
Subjects:
Freetext Keywords: Brain computer interface, Interfaz cerebro-ordenador, MEG, Multiobjective optimization, Optimización multiobjetivo, Classification, Clasificación, Feature subset selection, Selección de características del subconjunto, Probabilistic modeling, Modelo probabilístico.
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (1MB) | Preview

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.

More information

Item ID: 16447
DC Identifier: http://oa.upm.es/16447/
OAI Identifier: oai:oa.upm.es:16447
DOI: 10.1007/s00422-012-0506-6
Official URL: http://link.springer.com/article/10.1007%2Fs00422-012-0506-6
Deposited by: Memoria Investigacion
Deposited on: 15 Jul 2013 15:01
Last Modified: 21 Apr 2016 16:45
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM