Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.aca.2007.08.001
Title: Variable interaction network based variable selection for multivariate calibration
Authors: Rao, R. 
Lakshminarayanan, S. 
Keywords: Multivariate calibration
Multivariate statistics
Partial correlation
Partial least squares
Variable interaction network
Variable selection
Issue Date: 5-Sep-2007
Source: Rao, 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
Abstract: Multivariate 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.
Source Title: Analytica Chimica Acta
URI: http://scholarbank.nus.edu.sg/handle/10635/64779
ISSN: 00032670
DOI: 10.1016/j.aca.2007.08.001
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