## 7 Imbalanced Learning Models Result Analysis
### Oversampling-Based Imbalanced Learning Models Results Analysis
Invariably, determining the AMiDS models' probabilities of distinguishing between normal and anomalous examples under the different oversampling methods for both the training and testing data helps to determine how well the models could distinguish between the classes and whether the models could learn a generalizable pattern from the data to classify new data correctly or not. However, quantitatively determining the models' classification performances using metrics such as A, P, R, and F1 is essential to understand the usefulness and completeness of the obtained results. Additionally, determining the inference time is vital to assess the model's suitability to the application and type of problem. According to the weighted averages presented in Tables 4 and 7, the PCA-based AMiDS showed a superior performance when applying the KMS, ROS, and SMT with 97% A, P, R, and F1 for the testing data, but it had different values for the training data with SMT performing slightly better with 77% A, 84% P, 77% R, and 76% F1 compared to the KMS and ROS. A detailed interpretation of the results revealed that the percentage of correct predictions given the total number of predictions was 97% for the testing data compared to 77% for the training data. Furthermore, the ratio of true positives (anomalous) and total positives predicted was 97% for the testing data, compared with 84% for the true data. Further, the ratio of true positives to the number of total positives in the dataset was 97% for the testing data, compared to 77% for the training data, indicating that the model had a high level of sensitivity for the testing data compared to the training data. Among the examples that the model could identify, given the testing data, 97% of them were correct compared to 76% for the training data. When applying the LRS method, the PCA-based AMiDS performed more poorly than the KMS, ROS, and SMT methods. The percentage of correct predictions, given the total number of predictions, was 81% for the training data and 60% for the testing data. The ratio  
Figure 11: The Autoencoder (AE) model history and parameters for the PCA- and OSVM-based AMiDS models. Figure 12: Variational Autoencoder Oversampler (VAEOversampler) encoder and decoder parameters for the PCA- and OSVM-based AMiDS models. of true positives to total positives predicted was 85% and 90% for the training and testing data, respectively. However, the ratio of true positives and total positives in the dataset was 81% for the training data, compared to 60% for the test data. Given the harmonic mean, 80% of the examples that the model could identify were correct for the training data, compared to 71% for the testing data. While the performance of the PCA-based AMiDS was the worst when applying LRS compared to the other methods, it had an inference time of 0.21 s for the testing data, thus ranking it second after the ROS method, which recorded the lowest inference time of 0.18 s.  
According to the weighted averages presented in Tables 10 and 13, the oSVM-based AMiDS performance was dissimilar to the PCA-based AMiDS model. For example, it exhibited a poor performance when applying the KMS method compared to the PCA-based AMiDS model. However, it performed relatively better when applying the LRS, ROS, and SMT. The model could correctly predict 74% of the examples given the total number of predictions for the training data and 80% for the testing data using the LRS method. Additionally, the ratio of true positives to total positives predicted was 88% for the training data and 80% for the testing data, and the ratio of true positives to total positives in the data was 80% for the training data and 74% for the testing data. Furthermore, among all examples that the model could identify, it correctly predicted 80% of them for the training data and 81% for the testing data. The SMT method slightly improved the performance of the OSVM-based AMiDS. The percentages of correct predictions given the total number of predictions were 76% and 80% for the training and testing data, respectively.