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Predictive-property-ranked variable reduction in partial least squares modelling with final complexity adapted
Jan P M Andries1, Yvan Vander Heyden, Lutgarde M C Buydens
1Department of Life Sciences, Avans Hogeschool, University of Professional Education, Breda, The Netherlands.
Analytica Chimica Acta
|December 26, 2012
Summary
Variable reduction using the FCAM method improves partial least squares regression (PLS1) calibration. The regression coefficient (REG) and significance (SIG) properties are most effective for selecting informative variables with good predictive ability.
Area of Science:
- Chemometrics
- Analytical Chemistry
- Machine Learning
Background:
- Partial Least Squares regression (PLS1) calibration performance can be enhanced by eliminating uninformative variables.
- Existing variable-reduction methods rely on predictor-variable or predictive properties that can change during the reduction process.
- A novel method, Predictive Property-Ranked Variable Reduction with Final Complexity Adapted Models (PPRVR-FCAM or FCAM), was recently introduced.
Purpose of the Study:
- To investigate the utility and effectiveness of various predictor-variable properties within the FCAM method.
- To compare the selective and predictive performances of models generated using different properties.
- To identify optimal properties for variable reduction in PLS1.
Main Methods:
- Applied the FCAM backward variable elimination method to three datasets.
- Evaluated six individual properties: regression coefficient (REG), significance (SIG), norm of loading weight (NLW), variable importance in projection (VIP), selectivity ratio (SR), and squared correlation coefficient (COR).
- Investigated nine combined properties and statistically compared model performances using the one-tailed Wilcoxon signed rank test.
Main Results:
- Models derived from FCAM variable reduction showed similar or superior predictive abilities compared to full spectrum models.
- After mean-centering, REG and SIG properties identified fewer informative variables with relevant meaning, outperforming other individual properties in selectivity.
- SIG demonstrated the best selective ability among all individual and combined properties, with comparable predictive ability; REG was faster.
Conclusions:
- Variable reduction using the FCAM method is recommended with REG or SIG properties for PLS1.
- The selective ability of REG can be enhanced by combining it with NLW or VIP.
- FCAM effectively reduces variables, leading to improved or equivalent predictive performance in PLS1 models.
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