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To increase the accuracy of the system, a forward selection process was applied for each classification.
A sequential forward selection process is used to determine the optimum subset of features for two distinct classifiers, a Fisher Linear Discriminant FLDD) classifier and a quadratic classifier.
During the forward selection process, existing covariates whose p-values increased to greater than.05 were removed from the model.
Multivariate QTL are chosen in a forward selection process.
A forward selection process resulted in a similar model.
The significance level (chi-square score test) for the forward selection process was set to ≤0.1.
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Variables were screened using a univariable analysis and logistic multiple-regression models were developed in a forward-selection process.
When the number of trials was ≥20, predictors were initially screened in univariable models and then added in multivariable models using a forward-selection process, up to a maximum of one predictor per 10 trials.
First, a forward selection procedure was used.
Genes were selected for inclusion using a forward stepwise selection process with a significance criterion of 0.01 for entrance into the model.
Total lipids was forced into each model before running the forward stepwise selection process.
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