# Optimal Partitioning of Unbalanced Datasets for BGP Anomaly Detection
## 5 Results Analysis
### Overall Accuracy and Performance
Table 9 shows the comparative assessment of the weighted balanced measures and reflects the excellent performance of the proposed method by considering all measures of the evaluation. The proposed method was clearly superior to the conventional classifiers as well as the weighted ELM approach in achieving the highest accuracy, precision, recall, and F1-score at 94.52%, 95.18%, 94.48%, and 94.67%, respectively. For the accuracy assessment, the proposed method's 94.52% performance is 0.85 percentage points better than the best baseline (RF-SMOTE with 93.67%) and 4.00 percentage points better than the weighted ELM (90.52%). Such a large difference in the overall classification correctness is indicative of its usefulness in scenarios where weighted evaluation is applied. The precision assessment reveals an even greater lead, as the fusion method (95.18%) exceeded the performance of the RF-SMOTE (93.91%) by 1.27 percentage points and that of the weighted ELM (91.43%) by 3.75 percentage points, underscoring the excellent ability to reduce false positives under relative conditions. The recall metrics reflect the strong sensitivity performance of the proposed method, with the method yielding 94.48% compared to the RF-SMOTE's 93.08% and the weighted ELM's 90.84%. In the F1-score analysis, it can clearly be seen that the proposed method performed excellently in a balanced way as well, as its score of 94.67% whooped the best baseline (i.e., RF-SMOTE) with a net margin of 1.49 percentage points and the weighted \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Measure**} & \multicolumn{8}{c}{**Classifier**} \\ \cline{2-13}  & \multicolumn{3}{c}{**Random Forest**} & \multicolumn{3}{c}{**SVM**} & \multicolumn{3}{c}{**ANN**} & \multicolumn{3}{c}{**Weighted**} & **Proposed** \\ \cline{2-13}  & **–** & **SMOTE** & **Tomek** & **–** & **SMOTE** & **Tomek** & **–** & **SMOTE** & **Tomek** & **ELM** & **Method** \\ \hline Accuracy (\%) & 92.52 & 92.67 & 92.81 & 89.32 & 89.71 & 89.66 & 91.22 & 92.05 & 91.51 & 91.56 & 94.13 \\ \hline Precision (\%) & 91.03 & 91.96 & 91.25 & 87.04 & 88.13 & 87.51 & 90.71 & 91.15 & 90.27 & 90.15 & 93.22 \\ \hline Recall (\%) & 88.12 & 90.01 & 89.10 & 84.87 & 86.08 & 85.40 & 88.34 & 89.45 & 88.41 & 88.55 & 90.87 \\ \hline F1-Score (\%) & 89.55 & 90.97 & 90.16 & 85.94 & 87.09 & 86.44 & 89.50 & 90.29 & 89.33 & 89.34 & 92.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance comparison for balanced measures. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Measure**} & \multicolumn{8}{c}{**Classifier**} \\ \cline{2-13}  & \multicolumn{3}{c}{**Random Forest**} & \multicolumn{3}{c}{**SVM**} & \multicolumn{3}{c}{**ANN**} & \multicolumn{3}{c}{**Weighted**} & **Proposed** \\ \cline{2-13}  & **–** & **SMOTE** & **Tomek** & **–** & **SMOTE** & **Tomek** & **–** & **SMOTE** & **Tomek** & **ELM** & **Method** \\ \hline Accuracy (\%) & 92.47 & 93.67 & 92.86 & 89.37 & 90.07 & 89.62 & 91.21 & 91.89 & 91.85 & 90.52 & 94.52 \\ \hline Precision (\%) & 93.52 & 93.91 & 93.59 & 89.07 & 90.21 & 89.15 & 91.15 & 92.05 & 91.75 & 91.43 & 95.18 \\ \hline Recall (\%) & 92.17 & 93.08 & 92.45 & 88.10 & 88.74 & 88.45 & 89.28 & 89.78 & 89.41 & 90.84 & 94.48 \\ \hline F1-Score (\%) & 92.06 & 93.18 & 92.35 & 87.92 & 88.41 & 88.53 & 90.51 & 90.98 & 90.45 & 91.56 & 94.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparison for weighted balanced measures. ELM (1.56%) with an outstanding margin of 3.11 percentage points. Thus, we achieved a better performance across the board, showing that the proposed methodology is a sound solution to the nuances of weighted classification.