## 4 Results and Discussion
The proposed FL-BDE model, with a value of 1.88%, produced the best performance in terms of the misclassification rate (MCR), which represents the classification error in all of the positive and negative classes. When it is compared to the results produced by the ML- and EL-based models, this result corresponds to an extremely low value. In fact, it had an MCR of 1.87% less than the one (an MCR of 3.75%) of the model created using the RF method, which produced the closest value to that of our model. Given that the MCR is inversely correlated with the classification accuracy, it can be concluded that the proposed approach shows an excellent performance in this regard. In terms of the false positive rate (FPR), which indicates the proportion of negative instances that were predicted to be positive, \begin{table}
\begin{tabular}{c c} \hline \hline
**ML-Based Models** & **Setting Parameters** \\ \hline SVM method & Number of iterations: 15 \\ Lambda: 0.02 \\ \hline \multirow{4}{*}{LR method} & Optimization tolerance: \(1\times 10^{-8}\) \\  & L1 regularization weight: 1 \\  & L2 regularization weight: 20 \\  & Memory size for L-BFGS: 40 \\ \hline BPM method & Number of training iterations: 20 \\ \hline \multirow{4}{*}{BDT method} & Maximum number of leaves per tree: 8 \\  & Minimum number of instances per leaf node: 2 \\  & Learning rate: 0.2 \\  & Number of trees constructed: 64 \\ \hline \multirow{4}{*}{DF method} & Resampling method: Bagging \\  & Number of decision trees: 32 \\ \cline{1-1}  & Maximum depth of the decision trees: 128 \\ \cline{1-1}  & Number of random splits per node: 256 \\ \cline{1-1}  & Minimum number of instances per leaf node: 1 \\ \hline \multirow{4}{*}{NN method} & Number of hidden nodes: 30 \\ \cline{1-1}  & Learning rate: 0.125 \\ \cline{1-1}  & Number of learning iterations: 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The setting parameters and their values used in the Azure ML environment of the classifiers used in the multi- classification process. and the false negative rate (FNR), which indicates the proportion of positive instances that were predicted to be negative, the FL-BDE model also performed quite well. The results in Table 3 also show us that the proposed model's 2.75% FPR and 1% FNR values were considerably lower than the FPR and FNR values of the other models. Despite it having the same FNR value as the proposed model, the NN-based model performed the worst out of all of the models, since it had the greatest MCR and FPR values. The NN-based model achieved an MCR value of 10.63% and an FPR value of 20.25%. Figure 4 shows the performance results of the FL-BDE, ML- and EL-based models at the split ratio of 0.60. According to the performance values shown in Figure 4, the FL-BDE model offered the best performance among all of the models in terms of the accuracy, recall, specificity, precision and F-measure metrics. The proposed model achieved results with an accuracy of 0.9813, a recall of 0.9900, a specificity of 0.9725, a precision of 0.9730 and an F-measure of 0.9814. While the RF-based model was the most similar one the proposed model in terms of the accuracy and F-measure metrics, the LR-based model achieved a similar result, with values of 0.9700 and 0.9690 for the specificity and precision metrics. Of all of the models, the NN-based model exhibited the lowest performance in terms of the other metrics, except for recall, with values of 0.8938 accuracy, 0.7975 specificity, 0.8302 precision, and 0.9031 for the F-measure. However, this model provided the best performance in terms of the recall metric, along with the FL-BDE model, with a value of 0.9900. Figure 5 illustrates ROC curves that demonstrate the relationship between the false positive rates and true positive rates for the FL-BDE, ML- and EL-based models at the split ratio of 0.60. The value of the area under the ROC curve (AUC) gives a critical measure of the model's success in separating the classes.