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After constructing a classification model, based on this set of papers, the classifier applies the model to a MEDLINE sample.
The response variable was 0 for Ames- or 1 for Ames+ in these models and the predicted value found from the regression was used as a cutoff in constructing a classification model.
One of the most important steps of constructing a classification model is to encode each term by its essential properties.
When constructing a classification model, the experimental validated pre-miRNAs (real pre-miRNAs) are used as positive samples.
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Despite numerous attempts to determine the appropriate re-sampling proportion in each class by using a trial-and-error method in order to construct a classification model with imbalanced data (Barandela, Vadovinos, Sánchez, & Ferri, 2004; He, Han, & Wang, 2005; Japkowicz, 2000; McCarthy, Zabar, & Weiss, 2005), the optimal strategy for each class may be infeasible when using such a method.
A central problem in constructing the efficient classification system is identifying a representative set of features from which to construct a classification model for a particular task.
A 61-sample training set containing 36 TAA patients (24 males and 12 females) and 25 controls (7 males, 18 females) were used to select classifier genes and construct a classification model.
We used this subset of 928 genes, plus the 9 control genes, to construct a classification model using two prediction programs: PAM and SVM.
This constructs a classification model of product form design based on consumer preferences.
The SVM-RFE procedure constructs a classification model using all available features, and the least informative features for that particular model are eliminated.
The top-ranked features are then used in a Hidden Naïve Bayes classifier to construct a classification prediction model from these filtered attributes.
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