## 6 Results and Discussion
### Comparison of Proposed Greedy Decision Tree with Other Techniques
The GDT algorithms were also compared with the work of [PERSON] et al. [2], who employed the RF algorithm with k-fold validation. Their study did not highlight what sampling strategy was adopted. As such, the results from their study were adopted and compared with the GDT variants proposed herein, while using the same dataset as used by [PERSON] et al. [2]. Table 8 shows the results obtained with different GDT variants, as well as those of [PERSON] et al., AdaBoost, LogitBoost, GentleBoost, and RUSBoost. Note that [PERSON] et al. [2] used only three performance measures (recall, precision, and F1-score). With regard to these three performance measures, the results found for RF by [PERSON] et al. [2] and those for the proposed GDT variants were comparable and of the \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algorithm** & **Validation** & **Sampling** & \begin{tabular}{c} **Accuracy** \\ **\%** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ **\%** \\ \end{tabular} & \begin{tabular}{c} **Specificity** \\ **\%** \\ \end{tabular} & \begin{tabular}{c} **FAR** \\ **\%** \\ \end{tabular} & \begin{tabular}{c} **Precision** \\ **\%** \\ \end{tabular} & \
\begin{tabular}{c} **F1-Score** \\ **\%** \\ \end{tabular} \\ \hline GDT & K-Fold & Stratified & 99.999 & 100 & 99.998 & 0.00263 & 99.997 & 99.999 \\ \hline GDT & K-Fold & Random & 99.993 & 99.989 & 99.996 & 0.0052 & 99.994 & 99.992 \\ \hline GDT & Monte & Random & 99.993 & 99.995 & 99.993 & 0.011 & 99.980 & 99.990 \\ \hline AdaBoost & K-Fold & Stratified & 97.99 & 98.13 & 98.13 & 2.33 & 97.66 & 97.90 \\ \hline LogitBoost & K-Fold & Stratified & 98.73 & 98.80 & 98.80 & 1.46 & 98.53 & 98.66 \\ \hline GentleBoost & K-Fold & Stratified & 99.32 & 99.32 & 99.32 & 0.75 & 99.24 & 99.28 \\ \hline RUSBoost & K-Fold & Stratified & 88.78 & 90.77 & 90.77 & 11.44 & 88.55 & 89.64 \\ \hline SVC-RF &
\begin{tabular}{c} Not specified \\ \end{tabular} & Not specified & 98.8 & 97.91 & 98.18 & 0.02 & 98.27 & 97.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of GDT variants with SVC-RF of [PERSON] et al. [1] and other algorithms. same quality. Furthermore, the three GDT variants also produced almost the same results, indicating that the different validation and sampling strategies did not have an impact on the performance. In addition, the results produced by the other four algorithms were also comparable with those of GDT. Table 9 reflects the average training time and throughput of the proposed GDT. Note that [PERSON] et al. [1] and [PERSON] et al. [2] did not share the hyperparameter values they used in their respective studies. Therefore, it was not possible to reproduce their results, as both of their algorithms depend upon hyperparameters. It is clearly visible from the table that with respect to the average training time (in seconds), GDT results in much lower training times than all other algorithms for both datasets. The same level of performance is observed for throughput, where GDT has far higher throughput than the other algorithms. It should be noted that although better results were obtained using the proposed approach, these results only represent high performance in the simulated dataset. It is not necessary that this better performance would be reflected in real-world scenarios, as reported by similar studies [53; 54].