Linked Life Data

17605354

Source:http://linkedlifedata.com/resource/pubmed/id/17605354

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
7
pubmed:dateCreated
2007-7-3
pubmed:abstractText
Two feature selection approaches for multilevel mental fatigue electroencephalogram (EEG) classification are presented in this paper, in which random forest (RF) is combined with the heuristic initial feature ranking scheme (INIT) or with the recursive feature elimination scheme (RFE). In a "leave-one-proband-out" evaluation strategy, both feature selection approaches are evaluated on the recorded mental fatigue EEG time series data from 12 subjects (each for a 25-h duration) after initial feature extractions. The latter of the two approaches performs better both in classification performance and more importantly in feature reduction. RF with RFE achieved its lowest test error rate of 12.3% using 24 top-ranked features, whereas RF with INIT reached its lowest test error rate of 15.1% using 64 top-ranked features, compared to a test error rate of 22.1% using all 304 features. The results also show that 17 key features (out of 24 top-ranked features) are consistent between the subjects using RF with RFE, which is superior to the set of 64 features as determined by RF with INIT.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
0018-9294
pubmed:author
pubmed:issnType
Print
pubmed:volume
54
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1231-7
pubmed:dateRevised
2009-11-11
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
A feature selection method for multilevel mental fatigue EEG classification.
pubmed:affiliation
Department of Mechanical Engineering, National University of Singapore 117576, Singapore. shen@nus.edu.sg
pubmed:publicationType
Journal Article