Recall that we consider three different feature selection techniques, the Lasso, SNSS, and RDA, in combination with three different classification approaches: (1) using the same model for classification as for feature selection, (2) using the selected features in a KNN classifier, or (3) using the selected features in a Random Forests classifier.
Random forest algorithm was adopted to classify driving styles using the selected features.
A simple first order security function is designed using the selected features for classification.
However, they fail to consider the performance of some specific learning task, e.g. regression, by using the selected features.
By using the selected features, not only the number of features is reduced, but also the classification accuracy is increased to 100% for all the three mentioned artificial neural networks.
Using the selected features, we evaluated eight commonly used classification algorithms: Support Vector Machine (SVM) [42], Logistic Regression (LR) [43], Random Forest (RF) [44], Artificial Neural Networks (ANN) [45], Decision Trees (DT) [46], Naïve Bayes (NB) [47], and their boosted versions, BDT and BNB [48].
After the feature selection is completed, we parameterized the SVMs using the selected feature set and the same grid search as above.
Figure 1 Results obtained on the CHEMDNER test set, using a combination of a first-order and a second-order CRF model, trained using the selected feature set.
Table 3 shows the results for the ability of the BANN-trained classifier, developed using the selected feature subset, to distinguish between BRCA1/2 mutation carriers and non-carriers in the independent testing dataset.
This study uses the selected features as the input for a classifier.
In this approach, the algorithm uses the selected features and check whether there is a probe attack in the first layer called probe layer.
