## 7 Imbalanced Learning Models Result Analysis
### Ensemble-Based Imbalanced Learning Models Results Analysis  
Like the hybrid methods, the PCA-based AMiDS performed better than the oSVM-based AMiDS when applying imbalanced learning using the ensemble-based methods EENSMB and BALCAS. For example, given Figure 10, the model had a 64% chance of distinguishing between the normal and anomalous examples using the testing data for both ensemble-based methods. Using the training data, the model could distinguish between examples with a probability of 62%. The oSVM-based AMiDS had probabilities of 30% and 56% and 36% and 58% when using the EENSMB and BALCAS, respectively, for the testing and testing data. The oSVM-based AMiDS model's performance when using the BALCAS was much better than that when using the EENSMB; however, it performed relatively poorly compared to the PCA-based AMiDS for the same method. While it is common for the AUC value for the training data to be higher than the AUC value for the testing data, the gaps between the two values are relatively small. The gap between the AUC values for the testing and training data is generally attributable to random variation or sampling variability due to the statistical nature of the function used to calculate AUC when the testing AUC value is slightly higher than the training AUC value. Therefore, we attribute the small value to the models making an arbitrary distinction between a few examples among the total number of examples in the testing dataset. Given the training and testing data, the oSVM-based model's capacity to distinguish between the two types of examples when applying imbalanced learning using the EENSB method is poor. According to Figure 10, the model's probabilities of distinguishing between normal and anomalous examples for the training and testing data were 36% and 30%, respectively. On the other hand, the model exhibited a better performance when using the BALCAS method. The AUC values for the training and testing data were 0.60 and 0.56, respectively. Nonetheless, the model still suffered from the inability to learn a generalizable pattern. Consistent with the AUC and ROC curve results, the oSVM-based AMiDS model had a poor classification performance when applying the EENSMB method compared to the BALCAS method, which enabled the model to perform relatively better. The same was true for the other metrics. For example, the percentages of correct predictions, given the total number of predictions when applying the EENSMB method, were 36% and 29% for the training and testing data, respectively, compared to the BALCAS method, where the percentages were 60% and 82% for the training and testing data, respectively (see Tables 22 and 25). The rest of the metrics show similar values for the training data when applying the EENSMB method. The ratio of true positives predicted to the total number of true positives predicted and the total number of true positives in the dataset was 36%. Additionally, among all examples that the model could identify, the model predicted 36% of those examples correctly. However, for the testing data, the values fluctuated between small and relatively high. For example, the ratio of true positives predicted to the total number of true positives predicted was 83%. However, the ratio of true positives predicted to the total number of true positives in the dataset was only 29%. Further, among all examples that the model could identify, the model correctly predicted 41% of the examples. When applying the BALCAS method, the ratios of true positives correctly predicted to the total number of true positives predicted and the total number of true positives in the dataset were 62% and 60% for the training data and 91% and 82% for the testing data, respectively. The percentages of correctly predicted examples compared to the total number of examples that the model would have identified were 58% and 86% for the training and testing data, respectively.