## 6 Experimental Setup
### Experimental Runs of the Oversampling Methods
\begin{tabular}{c} **No. of** \\ **Examples** \\ \end{tabular}  \\ \hline ER-KMS\({}_{P}\) & 0.97 & 0.97 & 0.97 & 0.97 & 0.75 &... & 0.2249 & 400 \\ ER-LRS\({}_{P}\) & 0.60 & 0.90 & 0.60 & 0.71 & 0.534 &... & 0.2090 & 400 \\ ER-ROS\({}_{P}\) & 0.97 & 0.97 & 0.97 & 0.75 &... & 0.1820 & 400 \\ ER-SMT\( \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **A** & **P** & **R** & **F** & **AUC** & **Training Time (s)** & **Inference** & **No. of** \\ \hline ER-KMS\({}_{O}\) & 0.44 & 0.44 & 0.44 & 0.44 & 0.440 & 0.676 &... & 2393 \\ ER-LRS\({}_{O}\) & 0.80 & 0.80 & 0.80 & 0.80 & 0.789 & 0.727 &... & 2393 \\ ER-ROS\({}_{O}\) & 0.68 & 0.69 & 0.68 & 0.67 & 0.680 & 0.466 &... & 2393 \\ ER-SMT\({}_{O}\) & 0.76 & 0.76 & 0.76 & 0.764 & 0.456 &... & 2393 \\ ER-SVMSMT\({}_{O}\) & 0.52 & 0.57 & 0.52 & 0.53 & 0.493 & 0.256 &... & 1728 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Trained oSVM-based AMiDS evaluation results for the oversampling methods based on the weighted average. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **A** & **P** & **R** & **F** & **AUC** & **Training Time (s)** & **Inference** & **No. of** \\ \hline ER-KMS\({}_{P}\) &... & 0.97 & 1.0 & 0.99 &... &... &... & 378 \\ ER-LRS\({}_{P}\) &... & 0.95 & 0.61 & 0.75 &... &... &... & 378 \\ ER-ROS\({}_{P}\) &... & 0.97 & 1.00 & 0.99 &... &... &... & 378 \\ ER-SMT\({}_{P}\) &... & 0.97 & 1.00 & 0.99 &... &... &... & 378 \\ ER-SVMSMT\({}_{P}\) &... & 0.82 & 0.13 & 0.23 &... &... &... & 378 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Scored PCA-based AMiDS evaluation results for the oversampling methods based on the anomalous case. Figure 7: Receiver operating characteristic (ROC) curves for the trained and scored PCA-based AMiDS using oversampling methods based on the five experimental runs, respectively: (**a**) ER-KMS, (**b**) ER-LRS, (**c**) ER-ROS, (**d**) ER-SMT, and (**e**) ER-SVMSMT. Summary of the AUC values based on the experimental runs according to the applied methods is shown in (**f**). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **A** & **P** & **R** & **F** & **AUC** & \begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} & \
\begin{tabular}{c} **Inference** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **No. of** \\ **Examples** \\ \end{tabular} \\ \hline ER-KMS\({}_{\text{O}}\) & 0.37 & 0.85 & 0.37 & 0.50 & 0.302 &... & 0.039 & 400 \\ ER-LRS\({}_{\text{O}}\) & 0.74 & 0.88 & 0.74 & 0.81 & 0.393 &... & 0.043 & 400 \\ ER-ROS\({}_{\text{O}}\) & 0.79 & 0.92 & 0.79 & 0.84 & 0.633 &... & 0.044 & 400 \\ ER-SMT\({}_{\text{O}}\) & 0.80 & 0.91 & 0.80 & 0.85 & 0.593 &... & 0.039 & 400 \\ ER-SVMSMT\({}_{\text{O}}\) & 0.55 & 0.94 & 0.55 & 0.66 & 0.698 &... & 0.029 & 400 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Scored oSVM-based AMiDS evaluation results for the oversampling methods based on the weighted average. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **A** & **P** & **R** & **F** & **AUC** & \begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} & \
\begin{tabular}{c} **Inference** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **No. of** \\ **Examples** \\ \end{tabular} \\ \hline ER-KMS\({}_{\text{O}}\) &... & 0.45 & 0.47 & 0.46 &... &... &... & 1206 \\ ER-LRS\({}_{\text{O}}\) &... & 0.80 & 0.80 & 0.80 &... &... &... & 1206 \\ ER-ROS\({}_{\text{O}}\) &... & 0.74 & 0.56 & 0.64 &... &... &... & 1206 \\ ER-SMT\({}_{\text{O}}\) &... & 0.78 & 0.74 & 0.76 &... &... &... & 1206 \\ ER-SVMSMT\({}_{\text{O}}\) &... & 0.30 & 0.43 & 0.35 &... &... &... & 529 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Trained oSVM-based AMiDS evaluation results for the oversampling methods based on the normal case.