## 3 Results
### Classifier Performance  
Different combinations of connectivity metrics, graph kernels, and classifiers were implemented with and without cost-sensitive learning over the MIT CHB EEG dataset related to an epileptic seizure, considering an equal number of preictal and ictal temporal segment samples. Performance comparison in terms of accuracy, AUC, precision, recall, and F1-score has been shown in Table 1 and Figure 6. Figure 6 shows combinations with mPLV and Figure 6 shows combinations with the correlation coefficient.  
It was observed that the workflows mPLV-WL-DT and mPLV-WL-RF outperformed other combinations, while the performances of these two were comparable considering the investigated metrics.  
To understand the effect of employing cost-sensitive learning, a performance comparison of mPLV-WL-DT and mPLV-WL-RF workflows with and without cost-sensitive learning is presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Without**} & \multicolumn{4}{c}{**With**} \\  & \multicolumn{4}{c}{**Cost-Sensitive Learning**} & \multicolumn{4}{c}{**Cost-Sensitive Learning**} \\ \hline
**Workflow** & **Acc** & **AUC** & **Prec** & **Rec** & **F1** & **Acc** & **AUC** & **Prec** & **Rec** & **F1** \\ \hline
**mPLV-WL-DT** & 91.7 & 90.0 & 100 & 80.0 & 88.9 & 91.7 & 90.0 & 92.7 & 91.7 & 91.0 \\
**mPLV-WL-RF** & 83.3 & 97.0 & 80.0 & 80.0 & 80.0 & 83.3 & 95.7 & 83.3 & 83.3 & 83.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of mPLV-WL-DT and mPLV-WL-RF workflows with and without cost-sensitive learning.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Without**} & \multicolumn{4}{c}{**With**} \\  & \multicolumn{4}{c}{**Cost-Sensitive Learning**} & \multicolumn{4}{c}{**Cost-Sensitive Learning**} \\ \hline
**Methodology +** & & & & & & & & & & \\
**Kernel +** & **Accuracy** & **AUC** & **Precision** & **Recall** & **F1** & **Accuracy** & **AUC** & **Precision** & **Recall** & **F1** \\
**Classifier** & & & & & & & & & & & \\ \hline mPLV-WL-DT & 0.9167 & 0.90 & 1 & 0.8 & 0.8889 & 0.9167 & 0.9 & 0.9271 & 0.9167 & 0.9148 \\ mPLV-WL-RF & 0.8333 & 0.9714 & 0.8 & 0.8 & 0.8 & 0.8333 & 0.9571 & 0.8333 & 0.8333 & 0.8333 \\ mPLV-WL-SVC & 0.75 & 0.7286 & 0.75 & 0.60 & 0.6667 & 0.4167 & 0.5 & 0.1736 & 0.4167 & 0.2451 \\ mPLV-SD-DT & 0.5833 & 0.5857 & 0.5 & 0.6 & 0.5455 & 0.6667 & 0.6571 & 0.6667 & 0.6667 & 0.6667 \\ mPLV-SD-RF & 0.4167 & 0.4286 & 0.3333 & 0.4 & 0.3636 & 0.3333 & 0.3286 & 0.3333 & 0.3333 & 0.3333 \\ mPLV-SD-SVC & 0.5833 & 0.5571 & 0.5 & 0.4 & 0.4444 & 0.5833 & 0.5571 & 0.5729 & 0.5833 & 0.5741 \\ mPLV-RW-DT & 0.9167 & 0.9286 & 0.8333 & 1 & 0.9091 & 0.8333 & 0.8286 & 0.8333 & 0.8333 & 0.8333 \\ mPLV-RW-RF & 0.75 & 0.9143 & 0.75 & 0.6 & 0.6667 & 0.6667 & 0.68286 & 0.6667 & 0.6667 & 0.6667 \\ mPLV-RW-SVC & 0.5833 & 0.5571 & 0.50 & 0.4 & 0.4444 & 0.5833 & 0.5571 & 0.5729 & 0.5833 & 0.5833 \\ corrCoff-WL-DT & 0.4167 & 0.5 & 0.4167 & 1 & 0.5882 & 0.25 & 0.5 & 0.0625 & 0.25 & 0.1 \\ corrCoff-WL-RF & 0.4162 & 0.5 & 0.4167 & 1 & 0.5882 & 0.25 & 0.5 & 0.0625 & 0.25 & 0.1 \\ corrCoff-WL-SVC & 0.4162 & 0.5 & 0.4167 & 1 & 0.5882 & 0.25 & 0.5 & 0.0625 & 0.25 & 0.1 \\ corrCoff-SD-DT & 0.4167 & 0.5 & 0.4167 & 1 & 0.5882 & 0.5 & 0.5 & 0.05556 & 0.5 & 0.5143 \\ corrCoff-SD-RF & 0.4167 & 0.5 & 0.4167 & 1 & 0.5882 & 0.4167 & 0.4062 & 0.5556 & 0.4167 & 0.3963 \\ corrCoff-SD-SVC & 0.4167 & 0.5 & 0.4167 & 1 & 0.5882 & 0.3333 & 0.5 & 0.1111 & 0.3333 & 0.1667 \\ corrCoff-RW-DT & 0.4167 & 0.5 & 0.4167 & 1 & 0.5882 & 0.3333 & 0.5 & 0.1111 & 0.3333 & 0.1667 \\ corrCoff-RW-RF & 0.4167 & 0.5 & 0.4167 & 1 & 0.5882 & 0.3333 & 0.5 & 0.1111 & 0.3333 & 0.1667 \\ corrCoff-RW-SVC & 0.4167 & 0.5 & 0.4167 & 1 & 0.5882 & 0.3333 & 0.5 & 0.1111 & 0.3333 & 0.1667 \\ corrCoff-RW-SVC & 0.4167 & 0.5 & 0.4167 & 1 & 0.5882 & 0.3333 & 0.5 & 0.1111 & 0.3333 & 0.1667 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classifier performance.