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The results showed that the variables selection methods could select more significant variables and improve the model performance, especially for the GA-Si-PLS model with the best performance than other variables selection algorithms with RP = 0.9426 and RMSEP = 0.2980.
We detailed the various models of the variables selection and examined the basic steps that are used to select the cost-effective predictors.
Input variables selection is one of the important problems when developing simulation models.
Following the critical variables selection, the RSM was applied to further optimize the enzyme production.
This study addresses the increasingly encountered chal- lenge of variables selection.
It addresses the application of machine learning techniques to the problem of variables selection.
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Stepwise variable selection was used to select relevant baseline covariates.
Variable selection.
Stepwise criteria for variable selection.
No variable selection was performed.
This section describes the variable selection.
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