## 4 Experimental Results and Discussion
### Experimental Results and Analysis
To improve the quality and efficiency of the model performance, we again found it necessary to consider improving the extraction of relevant features and eliminate irrelevant redundant features. These results give an indication that the current feature selection approaches are not able to extract the best relevant features and eliminate irrelevant feature; thus, we come to a trade-off between the two best entropy feature selection approaches to improve the lack in each. Additionally, Figure 6, along with Table 6 using the IoTID20 dataset and Figure 7, along with Table 7 using the NLS-KDD dataset, summarizes the empirical outcomes of our model via different features selection methods. As can be clearly noticed, UMF and IMF-based IDSs exhibit better performance for all performance measures (i.e., accuracy, precision, recall, f1-measure, ROC area) over the other selected related studies. This result is attributed to the concept of set theory (intersection and union) that aims to choose the best features. A feature that is present in both approaches (IG and GR) or at least one of \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metrics** & **Classifier** & **All Features** & **IG\_20** & **GR\_20** & **IMF\_15** & **UMF\_25** \\ \hline \multirow{4}{*}{**Accuracy (\%)**} & ANN & 99.37 & 97.60 & 96.56 & 97.69 & 97.79 \\  & C4.5 & 99.40 & 99.59 & 99.44 & 99.30 & 99.70 \\  & Bagging & 99.39 & 99.79 & 99.83 & 99.79 & 99.79 \\  & kNN & 99.20 & 99.39 & 98.90 & 99.20 & 99.68 \\  & Ensemble & 99.66 & 99.56 & 99.56 & 99.80 & 99.84 \\ \hline \multirow{4}{*}{**False Positive Rate (\%)**} & ANN & 0.007 & 0.025 & 0.038 & 0.025 & 0.024 \\  & C4.5 & 0.006 & 0.004 & 0.006 & 0.007 & 0.003 \\  & Bagging & 0.006 & 0.002 & 0.002 & 0.002 & 0.002 \\  & kNN & 0.008 & 0.006 & 0.011 & 0.008 & 0.003 \\  & Ensemble & 0.004 & 0.005 & 0.005 & 0.002 & 0.002 \\ \hline \multirow{4}{*}{**Precision (\%)**} & ANN & 99.4 & 97.6 & 96.7 & 97.7 & 97.8 \\  & C4.5 & 99.4 & 99.6 & 99.4 & 99.2 & 99.7 \\  & Bagging & 99.4 & 99.8 & 99.8 & 99.8 & 99.8 \\  & kNN & 99.2 & 99.4 & 98.9 & 99.3 & 99.7 \\  & Ensemble & 99.7 & 99.6 & 99.6 & 99.8 & 99.8 \\ \hline \multirow{4}{*}{**Recall (\%)**} & ANN & 99.4 & 97.6 & 96.6 & 97.7 & 97.8 \\  & C4.5 & 99.4 & 99.6 & 99.4 & 99.3 & 99.7 \\  & Bagging & 99.4 & 99.7 & 99.8 & 99.8 & 99.8 \\  & kNN & 99.2 & 99.4 & 98.9 & 99.2 & 99.7 \\  & Ensemble & 99.7 & 99.6 & 99.6 & 99.8 & 99.8 \\ \hline \multirow{4}{*}{**Fl-Measure (\%)**} & ANN & 99.4 & 97.6 & 96.6 & 97.7 & 97.8 \\  & C4.5 & 99.4 & 99.6 & 98.9 & 99.3 & 99.4 \\  & Bagging & 98.8 & 99.8 & 99.8 & 99.8 & 99.7 \\  & kNN & 99.2 & 98.8 & 98.9 & 99.2 & 99.4 \\  & Ensemble & 99.7 & 99.6 & 99.1 & 99.8 & 99.8 \\ \hline \multirow{4}{*}{**AUC (\%)**} & ANN & 99.8 & 99.0 & 98.8 & 97.9 & 98.6 \\  & C4.5 & 99.6 & 99.8 & 99.8 & 99.8 & 99.8 \\ \cline{1-1}  & Bagging & 99.2 & 100 & 100 & 100 & 100 \\ \cline{1-1}  & kNN & 99.3 & 99.2 & 98.9 & 99.4 & 99.8 \\ \cline{1-1}  & Ensemble & 99.6 & 99.6 & 100 & 1 & 1.00 \\ \hline \multirow{4}{*}{**Training Time (s)**} & ANN & 814 & 300 & 280 & 90 & 210 \\ \cline{1-1}  & C4.5 & 180 & 200 & 180 & 40 & 110 \\ \cline{1-1}  & Bagging & 190 & 160 & 160 & 50 & 150 \\ \cline{1-1}  & kNN & 150 & 90 & 90 & 40 & 90 \\ \cline{1-1}  & Ensemble & 550 & 350 & 285 & 60 & 200 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance evaluation results for the different feature selection approaches with respect to five ML models using NSL-KDD. them indicates this feature is more relevant while at the same time, non-redundant. To sum things up, dimensionality reduction approaches improve the performance classification, but we must be assured that only relevant features are selected, and thus we offer our study here as an example of this claim.