These results demonstrated that the MI-SVR combination can quite satisfactorily measure the importance of variables, increase interpretability, reduce the risk of overfitting, decrease complexity and generate predictive models for high dimension of variables based on selected features for complicated processing systems.
In other words, categorization results based on selected features should have maximum within-class similarity and minimum between-classes similarity.
In the serial implementation of Random Forest, the depth first method is adopted to split the nodes based on selected features.
Based on selected qualitative features, motor's quality types are determined by using the fuzzy logic method.
For a given trained classifier, we tested at 250 consecutive time points of the maintenance interval (corresponding to 4.5 s after excluding the first and the last 250 ms of the maintenance interval) whether the trained classifiers could discriminate between indoor/outdoor scene maintenance based on selected TF features at that time point.
Second, a 3D model fitting scheme is designed based on selected visible facial feature points, which facilitates 3D face reconstruction without any effect from self-occlusion.
If, then selecting for the minimum error is trivial since it is based on selecting one feature, and this corresponds to the minimum error resulted using the decision stump.
* Hybrid approach1 SVM model based on amino acid composition domain features **Hybrid approach2 SVM model based on selected dipeptide composition and domain features We further developed tools for discriminating class-1 and class-2 aaRSs.
* Hybrid approach1 SVM model based on amino acid composition domain features ** Hybrid approach2 SVM model based on selected dipeptide composition and domain features In this study, we trained and tested our models on a dataset containing equal numbers of aaRSs and non-aaRSs.
It was also found, based on the selected features, that portions of the prefrontal cortex, orbitofrontal cortex, parietal lobe and insula regions provided the most discriminant features for classification, in line with results reported in previous studies.
It was also found that, based on the selected features, portions of the prefrontal cortex, orbitofrontal cortex, temporal lobe, and parietal lobe regions provided the most discriminant information for classification, in line with results reported in previous studies.
