## 6 Results and Discussion
### Comparison of Proposed Greedy Decision Tree with Other Techniques  
This experiment was focused on deciding on a suitable validation approach. For this purpose, the k-fold and Monte Carlo approaches were employed, while using random (unstratified) and stratified sampling. The assessment of the two validation approaches is motivated by past studies, such as the one by [PERSON] et al. [77]. The problem discussed by [PERSON] [77] was related to time series forecasting, where the results showed that the Monte Carlo approach produced more stable results. Similarly, [PERSON] [78] compares the two approaches, mentioning that the possible partitions in k-fold are limited by k, whereas the possible partitions are many in Monte Carlo. This implies that models trained using Monte Carlo show repeatable results, which is more desirable in a practical application. However, not much information can be implied about the nature of the data, and how the model works in the case of diverse data, which might occur in exceptional cases. For the latter case, k-fold seems more ideal. Therefore, in this study, both techniques were used. In addition, the work carried out in this experiment was also benchmarked against the techniques presented in the studies by [PERSON] et al. [1] and [PERSON] et al. [2], as well as many other techniques, namely AdaBoost [79], LogitBoost [79], GentleBoost [79], \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **Accuracy \%** & **Recall \%** & **Specificity \%** & **FAR \%** & **Precision \%** & **F1 Score \%** & **No. of Features** \\ \hline Ahuja & 98.8 & - & 98.18 & 0.02 & 98.27 & 97.65 & 23 \\ \hline CART & 99.990 & 99.984 & 99.994 & 0.009 & 99.990 & 99.987 & 16 \\ \hline Modified CART & 99.989 & 99.985 & 99.991 & 0.014 & 99.986 & 99.985 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of CART and modified CART algorithms with [PERSON] et al. [1]and RUSBoost [79]. It should be noted that the prior work reported in [PERSON] et al. [1] did not mention which validation approach was adopted, whereas [PERSON] et al. [2] mentioned using five-fold cross validation. The next issue was to decide whether to use stratified sampling or random sampling for the training and testing datasets. On the question of whether or not to stratify, a recent study [80] mentions that stratification has advantages over random sampling, but defining strata can be difficult with incomplete knowledge. In the case of the present study, a supervised learning approach is performed, which itself required previously classified data, on the basis of which the classification criteria are to be constructed using an ML model. However, in another study, [PERSON] and [PERSON] [81] addressed the topic of sampling and specified that stratified sampling is a better approach because it makes in-class differences more apparent. Furthermore, [PERSON] and [PERSON] [82] mention that stratification lessens experimental variance. Table 7 shows the results of GDT construction with k-fold and Monte Carlo, with and without stratification, while using the dataset proposed by [PERSON] et al. The best results achieved in the study of [PERSON] et al. (adopted from [1]) are also reflected in the table. With regard to the performance measures identified in Section 5 (excluding throughput), the results indicate that with the exception of FAR, the performance with respect to all measures is more or less the same. For FAR, GDT with k-fold and stratified sample produced the minimum value of 0.00263. Table 6 also provides the results produced by the hybrid SVC-RD algorithm of [PERSON] et al. [1] Note that [PERSON] et al. [1] did not specify what validation or sampling approaches were used. Therefore, the results were adopted as reported in their paper. Based on the comparison of the three variants of GDT with the SVC-RF, AdaBoost, LogitBoost, GentleBoost, and RUSBoost, it can be fairly claimed that all versions of GDT were better than SVC-RF and the other algorithms with respect to the six performance measures.