## 4 Experimental Results and Analysis  
Multiple benchmark and one synthetic dataset ([[https://github.com/atifrizwan91/Synthetic-Dataset/blob/main/synthetic-dataset.csv](https://github.com/atifrizwan91/Synthetic-Dataset/blob/main/synthetic-dataset.csv)]([https://github.com/atifrizwan91/Synthetic-Dataset/blob/main/synthetic-dataset.csv](https://github.com/atifrizwan91/Synthetic-Dataset/blob/main/synthetic-dataset.csv)), accessed on 3 April 2021) is used to evaluate the performance of proposed model. All the features of synthetic dataset are visualized in the Figure 11 and the datasets are listed in Table 1. The results are compared with conventional SVM and F-SVM in terms of accuracy. As it is clear from Table 1 that all datasets have different numbers of samples, dimensions, and classes. The reason for selecting these datasets is that the results of MEB-based F-SVM are also reported based on these datasets. All the datasets are best to evaluate and compare the performance of the proposed model. The synthetic dataset is generated by using the library of python called Keras. The parameters of the data generator are set in a way that there is no linear separability between the classes of the data. The relationship between all features of synthetic data is given in Figure 11. It is clear from the illustration that there is no linear separability between any feature of the dataset. Furthermore, the number of synthetic dataset samples is also high compared to all other benchmark datasets. To measure the effectiveness of our proposed solution, we compared our results with F-SVM [14]. For a fair comparison, we employed accuracy as a performance evaluation measure similar to the case of F-SVM. The proposed model is evaluated on both Linear and RBF kernel, and results are reported based on accuracy. All the benchmark datasets are openly available and discoverable online on the UCI machine learning repository. The results of WR-SVM based on linear kernel and RBF kernel is shown in Tables 2 and 3 respectively. \begin{table}
\begin{tabular}{c l c c c} \hline \hline
**ID** & **Dataset** & **\# of Samples** & **\# of Classes** & **\# of Dimensions** \\ \hline
1 & Iris & 150 & 3 & 4 \\
2 & Waveform3 & 5000 & 3 & 21 \\
3 & Glass & 214 & 6 & 9 \\
4 & USPS & 9298 & 10 & 257 \\
5 & Synthetic & 5000 & 10 & 15 \\
6 & Segment & 2310 & 7 & 18 \\
7 & Sonar & 208 & 2 & 60 \\
8 & Musk & 6598 & 2 & 166 \\
9 & Zoo & 101 & 7 & 16 \\
10 & Breast Cancer Dataset & 277 & 2 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the 10 UCI datasets used in experiments. \begin{table}
\begin{tabular}{c l c c c} \hline \hline
**ID** & **Dataset** & **SVM** & **F-SVM** & **WR-SVM** \\ \hline
1 & Iris & 96.67 \(\pm\) 0.34 & 98.00 \(\pm\) 0.56 & 98.90 \(\pm\) 0.23 \\
2 & Waveform3 & 87.40 \(\pm\) 0.59 & 87.80 \(\pm\) 0.31 & 88.86 \(\pm\) 1.05 \\
3 & Glass & 66.36 \(\pm\) 0.98 & 70.45 \(\pm\) 0.74 & 72.15 \(\pm\) 0.67 \\
4 & USPS & 97.89 \(\pm\) 0.21 & 98.80 \(\pm\) 0.10 & 98.91 \(\pm\) 0.09 \\
5 & Synthetic & 83.85 \(\pm\) 1.7 & 90.65 \(\pm\) 0.09 & 90.86 \(\pm\) 0.10 \\
6 & Segment & 90.61 \(\pm\) 1.06 & 92.60 \(\pm\) 0.41 & 93.4 \(\pm\) 0.47 \\
7 & Sonar & 81.43 \(\pm\) 0.63 & 85.71 \(\pm\) 1.17 & 90.47 \(\pm\) 0.96 \\
8 & Musk & 91.98 \(\pm\) 0.86 & 92.52 \(\pm\) 0.91 & 94.05 \(\pm\) 0.63 \\
9 & Zoo & 94.55 \(\pm\) 1.51 & 98.18 \(\pm\) 0.52 & 98.18 \(\pm\) 0.49 \\
10 & Breast Cancer Dataset & 97.36 \(\pm\) 0.85 & 98.68 \(\pm\) 0.13 & 98.78 \(\pm\) 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the average classification accuracy (%) of Linear WR-SVM by Linear SVM and Linear F-SVM. In all types of datasets, WR-SVM's performance is good compared to conventional SVM and F-SVM. The reason behind this performance is that the proposed model performs better than the other methods.