## 3 Material and Methods
### Evaluation Metrics
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Principal Components**} & \multirow{2}{*}{\
\begin{tabular}{c} **Performance** \\ **Metric** \\ \end{tabular} } & \multirow{2}{*}{**SVC**} & \multirow{2}{*}{**KNC**} & \multirow{2}{*}{**DNN**} &
\begin{tabular}{c} **Voting** \\ **Ensemble** \\ \end{tabular} \\ \hline
10 &
\begin{tabular}{c} Accuracy \\ \end{tabular} & 92.2295 & 93.6275 & 94.3928 & 93.9026 \\ \cline{2-5}  & Detection Rate & 94.8239 & 93.3035 & 94.5612 & 94.4074 \\ \cline{2-5}  & False Positive Rate & 10.3614 & 6.0488 & 5.7752 & 6.6015 \\ \cline{2-5}  & F1-score & 92.4216 & 93.6030 & 94.3989 & 93.9297 \\ \cline{2-5}  & AUC & 92.2312 & 93.6273 & 94.3929 & 93.9029 \\ \hline
12 &
\begin{tabular}{c} Accuracy \\ \end{tabular} & 92.2714 & 93.8300 & 94.4137 & 94.0465 \\ \cline{2-5}  & Detection Rate & 94.6183 & 93.3272 & 94.4708 & 94.1620 \\ \cline{2-5}  & False Positive Rate & 10.0930 & 5.6633 & 5.6437 & 6.0698 \\ \cline{2-5}  & F1-score & 92.4748 & 93.8206 & 94.4367 & 94.0743 \\ \cline{2-5}  & AUC & 92.2626 & 93.8319 & 94.4135 & 94.0460 \\ \hline
15 &
\begin{tabular}{c} Accuracy \\ \end{tabular} & 92.4558 & 94.1261 & 95.0827 & 94.3453 \\ \cline{2-5}  & Detection Rate & 94.4822 & 92.7937 & 94.9483 & 93.9240 \\ \cline{2-5}  & False Positive Rate & 9.5737 & 4.5394 & 4.7826 & 5.2327 \\ \cline{2-5}  & F1-score & 92.6109 & 94.0510 & 95.0797 & 94.3255 \\ \cline{2-5}  & AUC & 92.4542 & 94.1271 & 95.0828 & 94.3456 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics using extracted features on the test set. features in comparison to other methods, except for [21]. When comparing our ensemble model to the SVC radial bias function (SVC-rbf) employed by [21], our model proves to be simpler, more robust, and faster. The ensemble model simplifies the process by aggregating predictions from individual learners and then performs the final prediction, whereas SVC-rbf constructs intricate decision boundaries to capture complex non-linear data patterns. The Ensemble learner exhibits greater robustness, especially when diverse base models are used, whereas SVC-rbf is sensitive to the selection of hyperparameters. Additionally, the ensemble model is faster to train, whereas SVC-rbf requires significant time due to its non-linear transformation computations. Hence, it proves that our proposed hybrid dimensionality scheme is a good addition to anomaly detection research. The proposed research has several advantages. First, it reduces the number of features. Therefore, the model's complexity and processing time decrease. Second, considering that collective opinion is better than individual opinion, it employed a voting ensemble for classification, which uses predictions of individual classifiers and outputs a majority vote. Third, it presents performance metrics using three sets of principal components, proving that the best performance metrics are achieved when those components are used that capture the highest variance. Although a disadvantage of the proposed research is that using multiple classifiers takes more processing time, since we have reduced the number of features, this disadvantage is compensated.