Linked Life Data

16779401

Source:http://linkedlifedata.com/resource/pubmed/id/16779401

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2006-6-16
pubmed:abstractText
We propose a new feature selection algorithm, Guilt-By-Association (GBA), which uses hierarchical clustering based on feature correlations to eliminate redundant features. GBA can be used in conjunction with other algorithms to produce a feature selection routine that explicitly considers both the similarities between features and their individual discriminatory powers. In this preliminary study, a simple form of GBA was investigated on simulated proteomic data.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1942-597X
pubmed:author
pubmed:issnType
Electronic
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1114
pubmed:dateRevised
2009-3-9
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Guilt-By-Association feature selection applied to simulated proteomic data.
pubmed:affiliation
Department of Electrical and Computer Engineering, The University of Texas at Austin, TX, USA.
pubmed:publicationType
Journal Article