# Optimal Partitioning of Unbalanced Datasets for BGP Anomaly Detection
## 5 Results Analysis
### Overall Accuracy and Performance  
Table 4 shows that the proposed method consistently outperformed all the other techniques. The highest classification accuracy (98.81%) was achieved by the proposed method for Class A, which outperformed the weighted ELM (98.11%) and the best baseline model (RF-SMOTE (98.45%)). Class B follows a similar trend whereby the proposed method (91.98%) outperformed both the weighted ELM (90.14%) and the leading baseline (RF-SMOTE with 91.18%). The largest improvement can be seen in Class C, where the proposed method (89.75%) outperformed the previously weight-based ELM method (85.21%) by an order of magnitude signifying the improved performance in handling imbalanced/complex data distributions. For Class D, the proposed method (94.02%) again leads, followed by the weighted ELM (93.18%) and RF-SMOTE (93.19%). As illustrated in Table 5, the proposed approach achieved the best precision for all classes (A-F) consistently better than the other techniques. In Class A, its performance (98.27%) surpassed both the weighted ELM (97.77%) and the best baseline (the RF-SMOTE at 97.56%). Also, for Class B, the proposed method (90.81%) outperformed the weighted ELM (89.48%) and best baseline (the RF-SMOTE at 89.10%). With the exception of Class B, the proposed method enabled a significant gain in the performance of the model over the baseline weighted ELM, with the biggest improvement occurring in Class C (proposed: 87.04% vs. weighted ELM: 85.56%), which is a testament to the robustness of the proposed method against difficult/imbalanced data. In Class D, the discussed approach (93.14%) holds first place, followed by the weighted ELM (92.45%) and RF-SMOTE (91.89%). For Class E, the proposed method (95.22%) again outperformed, with the weighted ELM (93.88%) and RF-Tomek (94.51%) following behind. Lastly, for Class F, the selected approach (94.10%) remains superior to both the weighted ELM (93.41%) and RF-SMOTE (93.12%). Table 6 shows the comparison of the recall performances of all the models, where it can be observed that the proposed method outperformed in all six classes, A, B, C, D, E, and F. The proposed approach reached the highest recall values in all categories, indicating a lower false negative rate. It achieved a recall of 98.62% for Class A, which is far beyond that of the best baseline method, RF-SMOTE (98.52%), and much higher than that of the weighted ELM (91.98%). The pattern repeats for all classes, with the most notable performance improvements seen in Class C, where the weighted ELM obtained an 85.65% accuracy, while our model obtained 82.53%, and for Class D, where we achieved 91.62%, flagging this vs. 90.05%; however, Class B remained a particularly difficult and challenging class for performing better on. The results show particularly notable performance improvements in the hard classes (B, C and D), where the proposed method had 2-3 percentage point improvements compared to the weighted ELM. This suggests that either a more nuanced approach to the class imbalance or more complicated decision boundaries are at play. The overall performance in all the classes indicates strong generalization power. As shown in Table 7, the use of the proposed approach resulted in the highest F1-scores in all classes in the comprehensive evaluation of the F1-scores, especially in Class A (98.44%), Class B (90.06%), and Class E (94.66%). These results show marked improvements over both traditional methods and the weighted ELM method, thereby emphasizing the effectiveness of the approach in the precision-recall tradeoff. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{3}{*}{ \begin{tabular}{c} **Class** \\ **Name** \\ \end{tabular}