## 4 Results and Discussion
Such as the model we proposed, most of the other models achieved an error rate value that was below the 0.6 split ratio, while providing an error rate value that was above the 0.7 split ratio for this metric in five-fold cross-validation. The FNR of the FL-BDE model in the relevant validation conditions produced an error rate that was above the values obtained in both of the split conditions. However, in all of the conditions, it  
Figure 6: Performance results of FL-BDE, ML- and EL-based models at the split ratio of 0.70.  
obtained a result that was much lower than the error rate values of the other models, except for the NN model. Figure 9 illustrates the performance results of FL-BDE, ML- and EL-based models created using five-fold cross-validation. From the performance values depicted in the figure, the FL-BDE model performed considerably better than the ML- and EL-based models did in terms of the accuracy, recall, specificity, precision and F-measure metrics, as well as the performance results in the split ratios of 0.60 and 0.70. The proposed model achevied the results of an accuracy of 0.990, a recall of 0.981, a specificity of 0.999, a precision of 0.999 and an F-measure of 0.990. In fact, the model showed a better performance of 2.1% in  
Figure 8: Error rates of FL-BDE, ML- and EL-based models for 5-fold cross-validation. Figure 7: ROC curves of FL-BDE, ML- and EL-based models at the split ratio of 0.70.  
terms of the accuracy and 3% in terms of the specificity, precision and F-measure metrics than the BDTmodel did, which produced the most similar result to it, except for the recall metric. The RF was the model that has the most similar result to that of our model, with accuracy values of 0.962, a recall of 0.969, a specificity of 0.954, a precision of 0.955 and an F-measure value of 0.962 after those of the BDT model. As in the split ratios of 0.60 and 0.70, the NN model gave the worst performance for all of the metrics, except the recall metric. When the performance results of Figures 4 and 9 were compared, it can be understood that the FL-BDE based model tested in five-fold cross-validation conditions had a better performance values in terms of all of the metrics, except the recall metric at the split ratio of 0.60. When Figures 6 and 9 were examined, a similar situation can be observed for the performance results at the split ratio of 0.70 in terms of the specificity, precision and F-measure metrics. Figure 10 shows the ROC curves and AUC values obtained for the FL-BDE, ML- and EL-based models in the condition of five-fold cross validation. From the performance outputs that are depicted, it can be seen that the proposed model had a better performance than the other models did, with an AUC output of 0.997. As in the performance results of other metrics, the ensemble learning-based BDT model had the best performance after our model, with a value of 0.993 in terms of this metric. For the conditions of five-fold validation, the SVM model has the lowest AUC score among all of the models. In fact, the SVM showed the worst performance in both the random split conditions and the five-fold cross-validation conditions. The experimental results showed that the proposed FL-BDE model produced the best performance in terms of most performance metrics in the random split ratio conditions and the five-fold cross-validation conditions. Here, it was decided which classifiers used in the voting and routing process would be included in the positive and negative classifier groups using the experimental results of the classifier errors. When the performances of ML-based models in both the split conditions and the cross-validation were evaluated according to the average FPR and FNR error rates, it was found that the LR, BPM, and BDT-based models provided the best performances in terms of the classification of negative instances, while the NN, DF and SVM-based models had the best performances in classifying the positive instances. This information is critical in structuring the proposed model.