## 6 Experiments and Results
### Effect of the Oversampling Techniques on the Framework  
Different oversampling techniques were tested with different oversampling ratio values for the SLFN classification algorithm with \(k\) value of 3. The SMOTE, ADASYN, SVM-SMOTE, Borderline1-SMOTE, and Borderline2-SMOTE oversampling techniques with the oversampling ratio values of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1 were considered. Figure 8 shows the performance of the different oversampling techniques and the different values of the oversampling ratio in terms of GM. The oversampling ratio for each oversampling technique having the best GM value was considered for the next stage of comparisons. Based on the figure, the oversampling ratios of 0.9, 0.6, 0.9, 0.6, and 0.5 were considered for the SMOTE, ADASYN, SVM-SMOTE, Borderline1-SMOTE, and Borderline2-SMOTE oversampling techniques, respectively.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Technique** & **ACC** & \(\text{PREC}_{\text{I}}\) & \(\text{PREC}_{\text{N}}\) & \(\text{REC}_{\text{I}}\) & \(\text{REC}_{\text{N}}\) & \(\text{GM}\) \\ \hline SVM-\(k\)-means++\({}_{k=2}\) & 0.9709 & 0.9220 & 0.9731 & 0.5974 & 0.9965 & 0.77156 \\ \hline SVM-\(k\)-means++\({}_{k=3}\) & 0.9708 & 0.9177 & 0.9731 & 0.5976 & 0.9963 & 0.77161 \\ \hline SVM-\(k\)-means++\({}_{k=4}\) & 0.9700 & 0.9004 & 0.9732 & 0.5991 & 0.9955 & 0.7723 \\ \hline SGD-\(k\)-means++\({}_{k=2}\) & 0.9589 & 0.7692 & 0.9674 & 0.5133 & 0.9894 & 0.7127 \\ \hline SGD-\(k\)-means++\({}_{k=3}\) & 0.9657 & 0.8758 & 0.9694 & 0.5422 & 0.9947 & 0.7344 \\ \hline SGD-\(k\)-means++\({}_{k=4}\) & 0.9686 & 0.9332 & 0.9700 & 0.5498 & 0.9973 & 0.7405 \\ \hline LR-\(k\)-means++\({}_{k=2}\) & 0.9587 & 0.8457 & 0.9626 & 0.4359 & 0.9946 & 0.6584 \\ \hline LR-\(k\)-means++\({}_{k=3}\) & 0.9574 & 0.8083 & 0.9628 & 0.4403 & 0.9928 & 0.6612 \\ \hline LR-\(k\)-means++\({}_{k=4}\) & 0.9570 & 0.7905 & 0.9633 & 0.4491 & 0.9918 & 0.6674 \\ \hline SLFN-\(k\)-means++\({}_{k=2}\) & 0.9703 & 0.8435 & 0.9770 & 0.6591 & 0.9916 & 0.8084 \\ \hline SLFN-\(k\)-means++\({}_{k=3}\) & 0.9776 & 0.8216 & **0.9885** & **0.8318** & 0.9876 & **0.9064** \\ \hline SLFN-\(k\)-means++\({}_{k=4}\) & **0.9844** & **0.9647** & 0.9855 & 0.7854 & **0.9980** & 0.8854 \\ \hline NB-\(k\)-means++\({}_{k=2}\) & 0.3199 & 0.0862 & 1.0000 & 1.0000 & 0.2734 & 0.5228 \\ \hline NB-\(k\)-means++\({}_{k=3}\) & 0.3198 & 0.0861 & 1.  
Table 3 is a performance comparison of different oversampling techniques for the selected values of the oversampling ratio. The average and standard deviation values of ACC, PREC, REC, and GM for 10 runs are presented in the table in the form of [value \(\pm\) std]. The maximum values are determined in bold. As shown in the table, the best values for PREC\({}_{\text{N}}\), REC\({}_{\text{I}}\), and GM were observed for the SVM-SMOTE oversampling technique. Although SMOTE oversampling technique has good results for ACC, PREC\({}_{\text{I}}\), and REC\({}_{\text{N}}\), as discussed above, GM is the most important measure for imbalanced datasets. Further, Figure 9 illustrates the values of the GM measure for different oversampling techniques for 10 runs. It shows the minimum, maximum, mean, and standard deviation values for each technique for the best oversampling ratio value. The figure shows that the SVM-SMOTE has better values than other techniques; it has the best maximum, minimum, and mean values as well as the best standard deviation as the box values have the minimum height compared to the others. This indicates the stable and exceptional performance of SVM-SMOTE compared to the other oversampling techniques. Thus, based on the observations presented in Table 3 and Figure 9, the SVM-SMOTE oversampling technique was considered as the best oversampling technique for the framework for the IoTID20 dataset.