# Optimal Partitioning of Unbalanced Datasets for BGP Anomaly Detection
## 5 Results Analysis
### Overall Accuracy and Performance
 } & \multicolumn{6}{c}{**Accuracy (\%)**} \\ \cline{2-11}  & \multicolumn{3}{c}{**Random Forest**} & \multicolumn{3}{c}{**SVM**} & \multicolumn{3}{c}{**ANN**} & \multicolumn{3}{c}{**Weighted**} & **Proposed** \\ \cline{2-11}  & **–** & **SMOTE** & **Tomek** & **–** & **SMOTE** & **Tomek** & **–** & **SMOTE** & **Tomek** & **ELM** & **Method** \\ \hline A & 98.12 & 98.45 & 98.24 & 96.51 & 97.17 & 96.82 & 97.53 & 97.27 & 97.22 & 98.11 & 98.81 \\ \hline B & 89.85 & 91.18 & 90.5  
Our analysis shows similar performance trends for all classifiers. For the best baseline method, Random Forest with SMOTE resampling, the F-scores were 98.03% (Class A), 88.47% (Class B), and 93.78% (Class E). However, our method outperformed these results with 0.41-2.21 (%) higher accuracy for each class. The performance of the weighted ELM method was intermediate, as it often achieved a better performance than the two baseline models but lagged 1.67-3.55 percentage points behind the proposed approach in most of the considered classes. The overall improvement in the classification tasks is more evident for the more challenging datasets (Classes B, C, and D), reaching F-scores of 90.06%, 86.33%, and 92.37%, respectively. These are 1.55-2.32 percentage points better than the weighted ELM and 1.59-3.21 percentage points better than our best baseline methods. The averaged gain in all categories indicates that the proposed method is very effective at capturing both the precision and recall (critical challenges in many tasks) simultaneously. As shown in Table 8, in line with the average performance metrics, the proposed method consistently outperformed the existing methods for all the evaluation metrics, which demonstrates its superior performance. Particularly, it obtained the highest accuracy (94.13%), precision (93.22%), recall (90.87%), and F1-score (92.03%), which confirms its stable performance, superior to conventional classifiers, among others; moreover, it significantly outperformed the weighted ELM. The proposed method yielded an accuracy of 94.13%, which shows an improvement of 1.32 percentage points with respect to the best baseline (RF-Tomek, 92.81%) and also a 2.57 percentage improvement with respect to the weighted ELM (91.56%). The advantage, in this case, is important, as the other metric had a better general classification performance in all classes. Similar trends can also be seen in the evaluation of the precision, wherein the proposed method (93.22%) surpassed the RF-SMOTE (91.96%) by 1.26 percentage points  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{ \begin{tabular}{c} **Class** \\ **Name** \\ \end{tabular}  } & \multicolumn{6}{c}{**Recall (\%)**} \\ \cline{2-11}  & \multicolumn{3}{c}{**Random Forest**} & \multicolumn{3}{c}{**SVM**} & \multicolumn{3}{c}{**ANN**} & \multicolumn{3}{c}{**Weighted**} & **Proposed** \\ \cline{2-11}  & **–** & **SMOTE** & **Tomek** & **–** & **SMOTE** & **Tomek** & **–** & **SMOTE** & **Tomek** & **ELM** & **Method** \\ \hline A & 97.96 & 98.52 & 98.28 & 96.61 & 96.59 & 96.30 & 97.00 & 97.51 & 97.25 & 91.98 & 98.62 \\ \hline B & 85.07 & 87.85 &  
and the weighted ELM (90.15%) by 3.07 percentage points, considering the strong tendency of the proposed method in suppressing false positive results. As can be seen in the recall metrics, the sensitivity of the proposed method is high, showing 90.87%, which is higher than that of the RF-SMOTE, showing 90.01%, and that of the weighted ELM, showing 88.55%. Assessing the method in terms of the F1-score proves the balanced performance, where it achieved a score of 92.03%, which is greater than those of both the best baseline (RF-SMOTE (90.97%)) and weighted ELM (89.34%) by sizable margins. This method resulted in an overall advantage of 1.06-2.69 percentage points across all metrics, illustrating the method's ability to jointly optimize multiple performance dimensions.