## 6 Experimental Setup
### Experimental Runs of the Ensemble-Based Methods
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **A** & **P** & **R** & **F** & **AUC** & \begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} & \
\begin{tabular}{c} **Inference** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **No. of** \\ **Examples** \\ \end{tabular} \\ \hline ER-EENSMB\({}_{p}\) &... & 0.65 & 0.51 & 0.57 &... &... &... & 104 \\ ER-BALCAS\({}_{p}\) &... & 0.65 & 0.51 & 0.57 &... &... &... & 104 \\ ER-ENSNB\({}_{\mathcal{O}}\) &... & 0.38 & 0.38 & 0.38 &... &... &... & 85 \\ ER-BALCAS\({}_{\mathcal{O}}\) &... & 0.69 & 0.39 & 0.50 &... &... &... & 85 \\ \hline \hline \end{tabular}
\end{table} 
Table 23: Trained PCA- and oSVM-based AMiDS evaluation results for the ensemble-based methods based on the normal case. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **A** & **P** & **R** & **F** & **AUC** & \begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} & \
\begin{tabular}{c} **Inference** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **No. of** \\ **Examples** \\ \end{tabular} \\ \hline ER-EENSMB\({}_{p}\) &... & 0.60 & 0.72 & 0.65 &... &... &... &... & 104 \\ ER-BALCAS\({}_{p}\) &... & 0.60 & 0.73 & 0.66 &... &... &... & 104 \\ ER-EENSMB\({}_{\mathcal{O}}\) &... & 0.35 & 0.35 & 0.35 &... &... &... & 81 \\ ER-BALCAS\({}_{\mathcal{O}}\) &... & 0.56 & 0.81 & 0.66 &... &... &... & 81 \\ \hline \hline \end{tabular}
\end{table} 
Table 25: Scored PCA- and oSVM-based AMiDS evaluation results for the ensemble-based methods based on the weighted average. consisted of four experimental runs. We describe these four runs using similar notations to the ones stated previously. As a result, we use the following notations: ER-AE\({}_{P}\), ER-AE\({}_{O}\), ER-VAEOS\({}_{P}\), and ER-VAEOS\({}_{O}\), respectively. For the two AE experimental runs, we trained the AE using the normalized and encoded imbalanced PiB dataset after splitting the PiB into training and testing sets with 1600 and 400 examples, respectively. To build the AE, we used a shallow neural network consisting of an input layer, one hidden layer, and an output layer. The size of the input and output layers was 90. To build the encoder, latent space, and decoder, we used the Dense() function from Keras. To encode the input layer, we used the ReLU activation function, L1, or Lasso Regularization (L1-type) activity_regularization \(1\times 10^{5}\) with 64 units to specify the dimensionality of the latent space. Similar to the encoding space, we used the ReLU activation function to define the latent and encoding spaces. Further, we optimized the autoencoder using adadelta optimization and used the mean square error to define the loss function. Figure 11 shows the AE model's training history, layer type, output shape, and number of parameters, including the total and trainable parameters. As for the VAE's two experimental runs, we used the vae_oversampler API introduced in [68]. The API provides methods to generate synthetic data using a VAE and an oversampler. First, it builds the VAE using Keras to learn the probability distribution of the data and then samples the learned distribution to generate the minority examples. To instantiate the vae_oversampler, we set the number of epochs to 100, the input dimension to 90, the latent dimension to 45, the batch size to 64, the dimension to 45, the batch size to 64, and the minority class identity to 0 (i.e., normal class). We then sampled the data using the fit_resample() method, which we used to build the training dataset to train the AMiDS models. After learning the distribution and resampling, the training dataset, which consisted of 2992 samples, showed balanced normal and anomalous examples, each with 1496 samples. According to [68], the VAEOversampler initializes the encoder network using a ReLU activation function and defines the hidden layer's size using the mean and log of the latent variable.