## 4 Classification Effect on the Dataset
### Classification Effect on Binary Classification Dataset  
XGBoost has achieved remarkable results in many fields, so we want to use this model to classify epilepsy. In epilepsy detection, a high-precision model can distinguish between epilepsy patients and healthy people, helping doctors quickly and accurately determine whether patients have specific diseases and thereby accelerating the treatment process. Through automated and intelligent binary classification, decision-making efficiency can be significantly improved, and the time and cost of manual judgment can be reduced. Therefore, we first evaluated the effect of the framework proposed in this article on binary classification tasks. We first performed a binary classification by grouping \(A\) (healthy, eyes open) and \(B\) (healthy, eyes closed) as one category, labeled as Class 1. \(C\) (epileptic, opposite zone), \(D\) (epileptic, epileptogenic zone), and \(E\) (epileptic, seizure) were grouped together as another category, labeled as Class 2. The classification was performed using the XGBoost model. The performance of the model is summarized in Table 2, which presents several evaluation metrics. The confusion matrix diagram and ROC curve of the XGBoost model on the binary classification task are plotted in Figure 10 and Figure 11, respectively. \begin{table}
\begin{tabular}{l l} \hline \hline
**Feature** & \(\mathbf{p}\)**-Value** \\ \hline \(\rho_{VG}\) & \(5.47167\times 10^{-38}\) \\ \(D_{VG}\) & \(5.44559\times 10^{-1}\) \\ \(M_{VG}\) & \(3.59848\times 10^{-24}\) \\ \(\alpha_{VG}\) & \(5.47167\times 10^{-38}\) \\ \(C_{VG}\) & \(6.75227\times 10^{-77}\) \\ \(L_{VG}\) & \(2.50911\times 10^{-2}\) \\ \(G_{VG}\) & \(1.13543\times 10^{-18}\) \\ \(\rho_{HVG}\) & \(1.59894\times 10^{-93}\) \\ \(D_{HVG}\) & \(9.96596\times 10^{-65}\) \\ \(M_{HVG}\) & \(1.25973\times 10^{-32}\) \\ \(\alpha_{HVG}\) & \(1.59894\times 10^{-93}\) \\ \(C_{HVG}\) & \(3.52079\times 10^{-71}\) \\ \(L_{HVG}\) & \(1.73161\times 10^{-82}\) \\ \(G_{HVG}\) & \(8.53595\times 10^{-99}\) \\ \(\rho_{LPVG}\) & \(1.51657\times 10^{-46}\) \\ \(M_{LPVG}\) & \(8.10539\times 10^{-1}\) \\ \(M_{LPVG}\) & \(6.97230\times 10^{-24}\) \\ \(\alpha_{LPVG}\) & \(1.51657\times 10^{-46}\) \\ \(C_{LPVG}\) & \(4.33977\times 10^{-94}\) \\ \(L_{LPVG}\) & \(1.77361\times 10^{-3}\) \\ \(G_{LPVG}\) & \(1.87006\times 10^{-14}\) \\ \(\rho_{MFDM}\) & \(4.01871\times 10^{-12}\) \\ \(D_{MFDM}\) & \(9.98876\times 10^{-10}\) \\ \(M_{MFDM}\) & \(5.32264\times 10^{-8}\) \\ \(\alpha_{MFDM}\) & \(4.01871\times 10^{-12}\) \\ \(C_{MFDM}\) & \(3.14236\times 10^{-8}\) \\ \(L_{MFDM}\) & \(4.56050\times 10^{-8}\) \\ \(G_{MFDM}\) & \(1.13674\times 10^{-10}\) \\ \(\Delta_{\text{QG}}\) & \(1.55180\times 10^{-130}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The \(p\)-value of the computed features in our numerical experiments. Figure 11: The ROC curve of the XGBoost model in the binary classification task. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Method** & **Accuracy** & **Balanced Accuracy** & **F1 Score** & **Precision** & **Recall** & **Specificity** & **MCC** & **Cohen’s Kappa** & **Hamming Loss** & **Jaccard Index** \\ \hline VG & 0.882 & 0.879 & 0.901 & 0.908 & 0.893 & 0.865 & 0.755 & 0.755 & 0.118 & 0.820 \\ HVG & 0.934 & 0.931 & 0.945 & 0.944 & 0.947 & 0.915 & 0.862 & 0.862 & 0.066 & 0.896 \\ LPVG & 0.928 & 0.928 & 0.939 & 0.952 & 0.927 & 0.930 & 0.851 & 0.851 & 0.072 & 0.885 \\ MFDM & 0.840 & 0.833 & 0.867 & 0.864 & 0.870 & 0.795 & 0.666 & 0.666 & 0.160 & 0.765 \\ QG & 0.796 & 0.784 & 0.832 & 0.821 & 0.843 & 0.725 & 0.572 & 0.572 & 0.204 & 0.713 \\ Ours & **0.978** & **0.978** & **0.982** & **0.983** & **0.98** & **0.975** & **0.954** & **0.954** & **0.020** & **0.964** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model evaluation indicators in binary classification task. Figure 10: Confusion matrix diagram of the XGBoost model under a binary classification task.