Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.aca.2007.08.001
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dc.titleVariable interaction network based variable selection for multivariate calibration
dc.contributor.authorRao, R.
dc.contributor.authorLakshminarayanan, S.
dc.date.accessioned2014-06-17T07:51:13Z
dc.date.available2014-06-17T07:51:13Z
dc.date.issued2007-09-05
dc.identifier.citationRao, R., Lakshminarayanan, S. (2007-09-05). Variable interaction network based variable selection for multivariate calibration. Analytica Chimica Acta 599 (1) : 24-35. ScholarBank@NUS Repository. https://doi.org/10.1016/j.aca.2007.08.001
dc.identifier.issn00032670
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/64779
dc.description.abstractMultivariate calibration problems often involve the identification of a meaningful subset of variables, from a vast number of variables for better prediction of output variables. A new graph theoretic method based on partial correlations (variable interaction network-VIN) is proposed. Many well studied representative calibration datasets spanning different application domains are selected for investigating the performance. Partial least squares (PLS) regression models combined with variable selection techniques are employed for benchmarking the performance. Subsets of variables with different number of variables are retained for the final analysis after VIN selection and progressive prediction accuracies are used for comparison. VIN-PLS results show significant improvement in prediction efficiencies and variable subset optimization. Improvement of up to 45% over existing methods with significantly fewer variables is achieved using the new method. Advantages of VIN based variable selection are highlighted. © 2007 Elsevier B.V. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.aca.2007.08.001
dc.sourceScopus
dc.subjectMultivariate calibration
dc.subjectMultivariate statistics
dc.subjectPartial correlation
dc.subjectPartial least squares
dc.subjectVariable interaction network
dc.subjectVariable selection
dc.typeArticle
dc.contributor.departmentCHEMICAL & BIOMOLECULAR ENGINEERING
dc.description.doi10.1016/j.aca.2007.08.001
dc.description.sourcetitleAnalytica Chimica Acta
dc.description.volume599
dc.description.issue1
dc.description.page24-35
dc.description.codenACACA
dc.identifier.isiut000249681800004
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