Comparison of selection methods of explanatory variables in PLS regression with application to manufacturing process data

By J. Gauchi, P. Chagnon. 2001

121 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/52b54b0b65377d3a1e724c60277f16ff29fa622b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Santiago A. Bortolato
2. E. Arancibia
3. Natalia Sorol
4. Bjoern H Menze
5. Alejandro C. Olivieri
6. Jean-Michel Roger
7. B. Michael Kelm
8. Fred A. Hamprecht

In Biomedical Engineering

1. Santiago A. Bortolato
2. E. Arancibia
3. Natalia Sorol
4. Alejandro C. Olivieri
5. Jean-Michel Roger

In Control and Systems Engineering

1. Santiago A. Bortolato
2. E. Arancibia
3. Alejandro C. Olivieri
4. Natalia Sorol

Citations in the corpus

• Visible/near infrared-partial least-squares analysis of Brix in sugar cane juice: A test field for variable selection methods (2010, background): “Both theory and practice shows that improved analytical performance is achieved in PLS upon variable selection, hence the interest in this chemometric activity through the years [3–8].”
• A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data (2009, background): “Different from [33], however, we did not see a relation to the apparent degree of ill-posedness of the classification problem (i.”
• A comparison of three methods for estimating leaf area index of paddy rice from optimal hyperspectral bands (2011, unknown): “…is necessary before both PLS and SVM regression models can be used to establish relationships between remotely sensed data and vegetation properties (Kubinyi 1996; Martens and Martens 2000; Davies 2001; Gauchi and Chagnon 2001; Lazraq et al. 2003; Schmidtlein and Sassin 2004; Durbha et al. 2007).”
• CovSel: Variable selection for highly multivariate and multi-response calibration: Application to IR spectroscopy (2011, background): “In the context of PLS regression, a review can be found in [5].”
• Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes (2004, unknown): “This method was observed to be the most suitable feature selection procedure among 20 methods compared empirically [24].”