# Comparative Analysis of Classifiers for Classification of Emergency Braking of Road Motor Vehicles
## 5 Experimental Results
), the exact values evolved via GA are hard to interpret, due to both (i) the enormous complexity of the computational structures they define and (ii) the lack of any human-understandable logic--e.g., similar to that of the _\"canonical\" top-down_ problem-solving approaches--applied in the process of obtaining these solutions. However, some of the SVM evolved parameters are similar to the default parameters of the scikit-learn SVM [29] which will cause almost identical results for these classifiers. With regard to XGBoost, the max_depth parameter which was obtained via GA is similar to the one that was obtained empirically by the author of XGBoost [22]. Despite this, the usual mechanisms of adjustment of the parameters are more like an art than a science, and each problem requires its own unique combinations of the values of hyperparameters of the learning method. In this sense, as both a holistic and heuristic approach, GA contributed to the relatively fast, automated optimization of the hyperparameters of XGBoost. As show in Table 11, the evolved SVM classifier performed in the same way as the default SVM classifier on the testing set and showed very close results on the training set. \begin{table}
\begin{tabular}{c c} \hline \hline
**Allele (Hyperparameter)** & **Optimal Value Obtained via GA** \\ \hline eta & 0.374 \\ gamma & 6.6 \\ max\_depth & 3 \\ min\_child\_weight & 2 \\ subsample & 0.454546 \\ colsample\_bytree & 0.624 \\ n\_estimators & 96 \\ features & [mP,aR, mR, mP*aR] \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Best-evolved values of hyperparameters of XGBoost. Figure 11: Fitness convergence characteristics obtained from 100 independent runs of GA employed for evolution of optimal hyperparameters of SVM (**a**) and XGBoost (**b**), respectively. The red line indicated the average (over 100 runs) of the fitness value. \begin{table}
\begin{tabular}{c c} \hline \hline
**Allele (Hyperparameter)** & **Value Obtained via GA** \\ \hline C & 0.802 \\ kernel & rbf \\ gamma & 0.8 \\ coef0 & 9.0 \\ shrinkage & True \\ features & [mP,aR, mR] \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Best-evolved values of hyperparameters of SVM. The comparison between the evolved XGBoost and out-of-the-box XGBoost classifiers is shown in Table 12; XGBoost with evolved values of hyperparameters demonstrated better results on the testing set than XGBoost with the default values of these parameters [32]. With respect to each metric under consideration, the evolved XGBoost demonstrated the best results on both the training and testing sets (Table 13). This classifier also featured a higher generalization ability manifested by a superior fitness value (CV F-score). Due to the superiority of the evolved XGBoost classifier over the others, we integrated it into the braking assistant in the full-scale Forum-8 driving simulator. The sample dynamics of the accelerator and brake pedals in two cases of emergency braking--with and without automated braking activated by the driver-supporting agent--are shown in Figure 12. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **23-NN Method** & **Default (and Evolved) SVM** & **Evolved XGBoost** \\ \hline Accuracy & 0.95 & 0.95 & 0.9538 \\ Precision & 0.896 & 0.9058 & 0.917 \\ Recall & 0.951 & 0.939 & 0.939 \\ F-score & 0.923 & 0.9221 & 0.9277 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparison of best classifiers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{2}{c}{**Default SVM Classifier**} & \multicolumn{2}{c}{**Evolved SVM Classifier**} \\ \cline{2-5}  & **Training Set** & **Testing Set** & **Training Set** & **Testing Set** \\ \hline Accuracy & 0.9348 & 0.95 & 0.9396 & 0.95 \\ Precision & 0.9479 & 0.9058 & 0.9465 & 0.9058 \\ Recall & 0.8971 & 0.939 & 0.9105 & 0.939 \\ F-score & 0.9218 & 0.9221 & 0.9281 & 0.9221 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Performance of SVM classifiers on training and testing sets.