## 7 Imbalanced Learning Models Result Analysis
### Generative-Based Imbalanced Learning Models Results Analysis  
Invariably, the underlying reason behind selecting the generative methods, AE and VAEOS, for imbalanced learning is to train the model to predict unseen examples, i.e., new examples that are similar to the original but are not the original; hence, this is termed passive adversarial learning. We consider it passive because we do not generate examples in a perturbational manner to trick the model. Instead, we use the existing data to generate similar data points that are not the same. On the other hand, we view active adversarial learning as trying to trick the model into making incorrect predictions using perturbed examples. Given the AE method, both AMiDS models recognized the anomalous examples in the training and testing data but did not recognize the normal examples. In contrast, the two AMiDS models could recognize the normal examples when applying imbalanced learning using the VAEOS method. When considering the AE method and the AUC and ROC curve metrics shown in Figure 13, the PCA- and oSVM-based AMiDS models could not distinguish between the normal and anomalous examples for the training and testing data. The probability of the model was 50%. We attribute the results to the inability of the models to recognize normal examples. Figure 13: Receiver operating characteristic (ROC) curves for trained and scored PCA- and oSVM-based AMiDS using generative-based methods based on the four experimental runs, respectively: (**a**) ER-AE for PCA, (**b**) ER-VAEO for PCA, (**d**) ER-AE for oSVM, and (**e**) ER-VAEO for oSVM. Summary of the AUC values based on the experimental runs according to the applied methods is shown in (**c**) and (**f**) for the PCA- and oSVM-based AMiDS, respectively. While both AMiDS models performed the same in the case of the AE method, the two models exhibited different performances when applying imbalanced and passive adversarial learning using the VAEOS method. The PCA-based model had a probability of 94% for distinguishing between anomalous examples for the training data compared to a 50% probability for the testing data. However, the oSVM-based model had a probability of 61% for the training data and only a 26% chance of distinguishing between examples for the testing data. Analyzing the classification performance of the two generative methods based on the degree of separability and the probability of distinguishing between the two classes of examples is an essential step in assessing the classification capability of the models. However, looking at the other metrics provides a more granular look into the overall performance of the AMiDS models. To that effect, the values of the A, P, R, and F1-score metrics, when considering the weighted average, indicate that the PCA- and oSVM-based AMiDS exhibited an excellent classification performance for the training and testing data when applying the AE method. However, looking at the values of these metrics based on the normal case reveals that the AMiDS models can identify only the anomalous examples (see Tables 29 and 32). Given the inability of the AMiDS models to identify normal examples in the training and testing data, one could conclude that this impacted the model's ability to separate between examples and its probability of distinguishing between the normal and anomalous examples when looking at the AUC and ROC curve metrics. Considering the anomalous case for the training and testing data, the PCA- and oSVM-based models performed well. The ratio of correctly predicted true positives to the total number of positives predicted in the training and testing data was 94% for both models. However, the ratio of correctly predicted true positives to the total number of positives in the training and testing data was 100% for both models. Given all the examples for the training data, the percentage of correctly predicted examples was 91% for both models. For the testing data, the percentage was 97% for both models.