| Title |
Regularized logistic regression and multiobjective variable selection for classifying MEG data
|
|---|---|
| Published in |
Biological Cybernetics, August 2012
|
| DOI | 10.1007/s00422-012-0506-6 |
| Pubmed ID | |
| Authors |
Roberto Santana, Concha Bielza, Pedro Larrañaga |
| 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. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Canada | 1 | 3% |
| Brazil | 1 | 3% |
| Unknown | 26 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 24% |
| Researcher | 5 | 17% |
| Student > Master | 3 | 10% |
| Student > Doctoral Student | 2 | 7% |
| Professor | 2 | 7% |
| Other | 4 | 14% |
| Unknown | 6 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 13 | 45% |
| Engineering | 5 | 17% |
| Medicine and Dentistry | 2 | 7% |
| Agricultural and Biological Sciences | 1 | 3% |
| Mathematics | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 7 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 August 2012.
All research outputs
#15,248,503
of 22,673,450 outputs
Outputs from Biological Cybernetics
#500
of 673 outputs
Outputs of similar age
#104,584
of 164,813 outputs
Outputs of similar age from Biological Cybernetics
#6
of 8 outputs
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