## 7 Imbalanced Learning Models Result Analysis
### Hybrid-Based Imbalanced Learning Models Results Analysis  
Given the two hybrid methods, SMTEN and SMTTEK, the PCA-based AMiDS generally performed better than the oSVM-based AMiDS when fitting the testing data. Based on Figure 10, the AUC value for the training and testing data when considering the PCA-based model was 0.75 for both methods, and the gap between the AUC for the testing and training data was slight. Thus, the model could learn a generalizable pattern from the training data. On the other hand, the oSVM-based AMiDS showed a poor performance when applying the SMTEN method. The AUC value for the testing data was 0.41 compared to the AUCvalue of 0.62 for the training data. The trained model had a 62% chance of distinguishing between normal and anomalous examples. However, the tested model could not learn a generalized pattern or, perhaps, exhibited overfitting. When applying the SMTTEK method, the oSVM-based AMiDS exhibited a slightly better performance with a 60% probability of distinguishing between examples using the testing data. Given the training data, the model could distinguish between examples with a probability of 76%.  
To analyze the classification performance of the AMiDS models, we considered the values of the other standard ML metrics and inference times recorded in Tables 16 and 19. The percentages of correct predictions given the total number of predictions for the PCA- and oSVM-based AMiDS models when applying the SMTEN method on the training data were 77% and 62%, respectively. When we applied the testing data, the percentages were 97% and 58%, respectively.  
The ratios of true positives to the total number of positives predicted for the PCA-based AMiDS for the training and testing data were 84% and 97%, respectively. For the oSVM-based AMiDS, the ratios were 62% and 88% for the training and testing data, respectively. When applying the training data, the ratios of true positives to the number of true positives were 77% and 62% for the PCA- and oSVM-based AMiDS, respectively. When we applied the testing data, the ratios were 97% and 58%, respectively. Finally, among all the examples that both models could identify, they correctly predicted 76% (PCA-based) and 62% (oSVM-based) when applying the training data and 97% (PCA-based) and 69% (oSVM-based) when applying the testing data.  
When applying the SMTEK method using the training data, the percentages of correct predictions given the total number of predictions were 77% and 76% for the PCA- and oSVM-based AMiDS, respectively. For the testing data, the percentages were 97% and 80%, respectively. The ratios of true positives to the total true positives predicted were 84% and 76% for the PCA- and oSVM-based AMiDS, respectively. In addition, the ratios of true positives to the total number of true positives in the data were 77% and 76%, respectively. However, among all examples that the models could identify, both models correctly predicted 76% of the examples.  
When applying the testing data, the PCA- and oSVM-based AMiDS could predict 97% and 80% of all predicted examples correctly. The ratios of the true positives given the total number of true positives predicted and all true positives in the dataset were 91% and 80% for the oSVM-based AMiDS, respectively. The PCA-based AMiDS reported a value of 97% for both cases. Additionally, among all examples that the models could identify, the PCA-based AMiDS correctly predicted 97% of the examples compared to the oSVM-based model, which was able to predict 85% of them correctly.  
While the ML metrics suggest that the PCA-based AMiDS had a better classification performance than the oSVM-based AMiDS, it incurred a slightly higher inference time than the oSVM-based AMiDS, if we consider the difference to be significant in the context of the application domain. Considering the inference time for the testing phases, the oSVM-based AMiDS inference times were 0.04 and 0.06 s for the SMTN and MSTEK, respectively. For both methods, The PCA-based AMiDS inference times were 0.24 and 0.21 s, respectively.