Linked Life Data

20152399

Source:http://linkedlifedata.com/resource/pubmed/id/20152399

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2010-2-15
pubmed:abstractText
Multivariate spectral analysis has been widely applied in chemistry and other fields. Spectral data consisting of measurements at hundreds and even thousands of analytical channels can now be obtained in a few seconds. It is widely accepted that before a multivariate regression model is built, a well-performed variable selection can be helpful to improve the predictive ability of the model. In this paper, the concept of traditional wavelength variable selection has been extended and the idea of variable weighting is incorporated into least-squares support vector machine (LS-SVM). A recently proposed global optimization method, particle swarm optimization (PSO) algorithm is used to search for the weights of variables and the hyper-parameters involved in LS-SVM optimizing the training of a calibration set and the prediction of an independent validation set. All the computation process of this method is automatic. Two real data sets are investigated and the results are compared those of PLS, uninformative variable elimination-PLS (UVE-PLS) and LS-SVM models to demonstrate the advantages of the proposed method.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
1873-3573
pubmed:author
pubmed:copyrightInfo
Copyright (c) 2009. Published by Elsevier B.V.
pubmed:issnType
Electronic
pubmed:day
15
pubmed:volume
80
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1698-701
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Variable-weighted least-squares support vector machine for multivariate spectral analysis.
pubmed:affiliation
State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't