## 7 Results and Analysis
### Numerical Results  
Figure 9 shows the most weighted features concluded by the Filter selection method using the ReliefF algorithm. The features with the positive weights contribute to the successful calls while the features with the negative weights contribute to the failed calls. Moreover, it demonstrated a computational time around 2.2 min. Figure 9: Filter method: Features weights. On the other hand, after applying the Wrapper feature selection method, the number of features concluded were 34 features. The duration taken for running WOA is 30 min and 34 s in 100 iterations. Meanwhile, the duration taken for SMA is 20 min and 40 s in 100 iterations. Moreover, WOA reached a fitness value of 0.025 while SMA reached a fitness value of 0.024, as shown in Figure 10.  
By comparing the two feature selection methods, the wrapper method selects fewer features with higher computational times. As shown in Figure 11, the prediction accuracy of the Decision Tree classifier reached 90.7% using the Wrapper selection method, while it reached 90.5% using the Filter selection method. This low prediction accuracy is due to the multicollinearity problem, as the decision tree makes no assumptions on relationships between features. However, in IMS, there is a strong relation between the features that lead to a call failure/call drop. For naive Bayes, the prediction accuracy reached around 92% accuracy for the Wrapper feature selection method and 91.5% for the Filter feature selection method. This can be  
Figure 11: Prediction accuracy. Figure 10: Wrapper algorithms: Fitness values. explained by the fact that naive Bayes assign zero probability to any prediction that does not have a specific value. This leads to the removal of any calls that have an empty data in the dataset traces. Moreover, this is the main drive behind the low prediction accuracy obtained compared to the KNN. In KNN, the prediction accuracy of 95% is achieved by using Wrapper feature selection method. Meanwhile, it reaches 93% by using filter feature selection method. The high accuracy of the KNN classifier is due to the fact that the KNN takes into consideration the correlation between the features, and the used dataset has a high dependency between its features. In SVM, the highest prediction accuracy of 97.5% is achieved by using the Wrapper feature selection method. Meanwhile, by using filter feature selection method it reaches 96.8%. Figure 11 shows that the Wrapper selection method outperforms the Filter selection method in terms of accuracy when combined with any of the four classifiers. This occurred due to the high capability of Swarm Algorithms that are present in Wrapper methods to solve complex problems, and this condition meets our dataset specs. Moreover, the main disadvantage of the filter selection methods is ignoring the interaction between features, so it considers each feature specifically and IMS CDRs contain highly interacted data; thus, this, explicitly, is the main reason beyond better performance. Moreover, we evaluate the performance of the four classifiers without using feature selection. The prediction accuracies for decision tree, naive Bayes, KNN and SVM are 90.5%, 92% and 95% and 96%, respectively. This shows that each classifier reached its lowest prediction accuracy when it does not use feature selection. SVM shows the highest prediction accuracy; however, in some cases, prediction accuracy is not reliable. In this case study, our data set does not contain equal distribution for successful and failed calls, as successful calls represent the higher percentage. Therefore, it might have happened that SVM shows the highest prediction accuracy due to its right prediction of only successful calls. In order to evaluate the model with the highest prediction accuracy (SVM), we are going to use the confusion matrix and Receiver Operating Characteristic (ROC).