Linked Life Data

18358856

Source:http://linkedlifedata.com/resource/pubmed/id/18358856

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2008-3-24
pubmed:abstractText
An improved method based on an ensemble of Monte Carlo uninformative variable elimination (EMCUVE) is presented for wavelength selection in multivariate calibration of spectral data. The proposed algorithm introduces Monte Carlo (MC) strategy to uninformative variable elimination-PLS (UVE-PLS) instead of leave-one-out strategy for estimating the contributions of each wavelength variable in the PLS model. In EMCUVE wavelength variables are evaluated by different Monte Carlo uninformative variable elimination (MCUVE) models. Moreover, a fusion of MCUVE and the vote rule can obtain an improvement over the original uninformative variable elimination method. Results obtained from simulated data and real data sets demonstrate that EMCUVE can properly carry out wavelength selection in the course of data analysis and improve predictive ability for multivariate calibration model.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1873-4324
pubmed:author
pubmed:issnType
Electronic
pubmed:day
7
pubmed:volume
612
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
121-5
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
An ensemble of Monte Carlo uninformative variable elimination for wavelength selection.
pubmed:affiliation
State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't