## 3 Results
### GP for System Setting Recognition
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Iter.** & \multicolumn{3}{c}{**Classification**} & \multicolumn{3}{c}{**Clustering**} \\ \hline
18 & 0.999849 & 0.999887 & \(f_{Skew}(y.\ a2)+f_{RMS}(x.\ a1)\) & 0.826849 & 0.840800 & \(f_{RMS}(y.\ a3)\times f_{Skew}(y.\ a2)+f_{Mean}(z.\ a2)\) \\
19 & 0.999735 & 0.999887 & \(f_{Skew}(y.\ a2)+f_{RMS}(x.\ a1)\) & 0.826354 & 0.840800 & \(f_{RMS}(y.\ a3)\times f_{Skew}(y.\ a2)+f_{Mean}(z.\ a2)\) \\
20 & 0.999811 & 0.999924 & \(f_{Skew}(y.\ a2)+f_{RMS}(x.\ a1)\) & 0.826354 & 0.840800 & \(f_{RMS}(y.\ a3)\times f_{Skew}(y.\ a2)+f_{Mean}(z.\ a2)\) \\
21 & 0.958472 & 0.999924 & \(f_{Skew}(y.\ a2)+f_{RMS}(x.\ a1)\) & 0.826937 & 0.840800 & \(f_{RMS}(y.\ a3)\times f_{Skew}(y.\ a2)+f_{Mean}(z.\ a2)\) \\
22 & 0.999849 & 0.999924 & \(f_{Skew}(y.\ a2)+f_{RMS}(x.\ a1)\) & 0.826929 & 0.840800 & \(f_{RMS}(y.\ a3)\times f_{Skew}(y.\ a2)+f_{Mean}(z.\ a2)\) \\
23 & 0.999849 & 0.999924 & \(f_{Skew}(y.\ a2)+f_{RMS}(x.\ a1)\) & 0.825289 & 0.840800 & \(f_{RMS}(y.\ a3)\times f_{Skew}(y.\ a2)+f_{Mean}(z.\ a2)\) \\
24 & 0.999849 & 0.999924 & \(f_{Skew}(y.\ a2)+f_{RMS}(x.\ a1)\) & 0.817839 & 0.840800 & \(f_{RMS}(y.\ a3)\times f_{Skew}(y.\ a2)+f_{Mean}(z.\ a2)\) \\
25 & 0.661087 & 0.999924 & \(f_{Skew}(y.\ a2)+f_{RMS}(x.\ a1)\) & 0.826273 & 0.840800 & \(f_{RMS}(y.\ a3)\times f_{Skew}(y.\ a2)+f_{Mean}(z.\ a2)\) \\
26 & 0.999887 & 0.999924 & \(f_{Skew}(y.\ a2)+f_{RMS}(x.\ a1)\) & 0.826142 & 0.840800 & \(f_{RMS}(y.\ a3)\times f_{Skew}(y.\ a2)+f_{Mean}(z.\ a2)\) \\
27 & 0.999735 & 0.999924 & \(f_{Skew}(y.\ a2)+f_{RMS}(x.\ a1)\) & 0.82639 & 0.840800 & \(f_{RMS}(y.\ a3)\times f_{Skew}(y.\ a2)+f_{Mean}(z.\ a2)\) \\
28 & 0.999887 & 0.999924 & \(f_{Skew}(y.\ a2)+f_{RMS}(x.\ a1)\) & 0.826377 & 0.840800 & \(f_{RMS}(y.\ a3)\times f_{Skew}(y.\ a2)+f_{Mean}(z.\ a2)\) \\
29 & 0.999849 & 0.999924 & \(f_{Skew}(y.\ a2)+f_{RMS}(x.\ a1)\) & 0.826352 & 0.840800 & \(f_{RMS}(y.\ a3)\times f_{Skew}(y.\ a2)+f_{Mean}(z.\ a2)\) \\
30 & 0.961797 & 0.999924 & \(f_{Skew}(y.\ a2)+f_{RMS}(x.\ a1)\) & 0.826352 & 0.840800 & \(f_{RMS}(y.\ a3)\times f_{Skew}(y.\ a2)+f_{Mean}(z.\ a2)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._The DT and the k-NN are trained to evaluate the performance of the constructed features. Then, the obtained model is applied to the trajectory F5 and prediction accuracy is computed. Both classification-based and clustering-based GPs provide high accuracy prediction during the training phase, while the accuracy is relatively low when applying the models to the testing trajectory. DT and k-NN are also applied to the whole set of extracted features to evaluate the effect of feature construction on classification accuracy. Finally, GP-based feature construction is also compared with the Principal Component Analysis (PCA) (feature learning) [40] and the ReliefF (feature selection) [41]. For comparison, one only feature is selected for both PCA and ReliefF. Table 8 summarizes the obtained results, where the training accuracy is computed on trajectories F1-F4 and the testing accuracy on trajectory F5. It can be seen that the training accuracy is lower than that provided by GP-based features, meaning that both the Principal Component (PC) constructed through the PCA and the feature selected through the ReliefF algorithm are not able to separate the two classes well. In addition, the model accuracy prediction on test 5 is lower than that obtained by applying the models trained with GP-based features. Similarly, although considering all features as input of classification models provides the best training accuracy, the testing accuracy is low, and the training time is higher, especially with k-NN. Figure 5: Constructed features through (**a**) classification-based GP and (**b**) clustering-based GP. The black line refers to the first setting trajectories (F1–F2). The red line refers to the second setting trajectories (F3–F4).