4.1.5. SVR Classifier

Hassanien et al. [133] introduced EHO to adjust the regression of emotional states for a support vector regression (SVR) repressor (SVR-EHO). In this method, the feature selection was adapted and the SVR classifier parameters were adjusted by using EHO, which provided a fast regression rate. The SVR-EHO approach was verified on the open database for emotion detection. The results of emotion regression on the SVR classifier indicated that SVR-EHO significantly improved regression accuracy.

Hassanien et al. [134] used two technologies, EHO and SVM (EHO-SVM), to develop a hybrid approach for automatic electrocardiogram (ECG) signal classification. The proposed approach included three modules, which were the efficient preprocessing module, feature extraction module, and feature classification module. EHO-SVM was utilized to optimize the features and parameters. The experiments showed that EHO-SVM achieved accurate classification results in terms of five statistical indices.

Tuba et al. [135] used the EHO algorithm to adjust the SVM parameter. The proposed approach was tested on standard datasets and the results were obtained by EHO and compared with two other approaches, which were the GA [41] and the grid search method (Grid). The computational experiments concluded that the EHO algorithm outperformed the GA [41] and Grid in the accuracy of classification for the same test problems.

Tuba et al. [136] used the EHO algorithm to find the optimal parameters of the SVM. In the proposed approach, the parameters of SVM were adjusted by EHO. Four different experiments based on a standard dataset were carried out. The simulation results showed that the performance of the proposed method achieved better results than the other strategies in all cases.
