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Best predictions were obtained when vis NIR and MIR were combined and important spectral features were selected through variable importance projection (VIP) approach.
For each index, two different models were calibrated: 1) based on all the features in spectra and 2) based on the features obtained by variable importance projection, which we denote by vis NIRvip, MIRvip, and (vis NIR + MIR)vip.
We evaluated the potential biomarkers by variable importance projection (VIP).
To refine this analysis, the variable importance projection (VIP) was obtained [ 28].
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A meteorological variable was declared important when 1) its variable importance in projection (VIP) was greater than 0.83 and 2) the cumulative variance explained by that meteorological observation by the top two PLSR components was at least 40%.
In this paper, we introduce a variable selection procedure, called the PLSVS method, to screen active effects in mixed-level SSDs based on the variable importance in projection which is an important concept in the partial least-squares regression.
Variable Importance in Projection (VIP) scores generated by this program estimate the importance of each variable in the projection used within the PLS model.
For this purpose, spots were ranked by the PLS-dependent variable importance on projection (VIP) score in this study and the most important spots were selected for prediction (28).
To determine the few ideal features with the best estimation accuracy, partial least squares regression (PLSR) and variable importance in projection (VIP) were applied to predict LAI values.
The Variable Importance in Projection scores of the PLS-DA model provided deeper insight into the differences between the two investigated states.
Weighted by the Vp (variable importance in projection) index, which indicates the importance of the sensor variables to the quality variables, the new monitoring statistic, Qv, is developed to ensure that the most vital sensor faults be detected successfully.
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