## 5 Discussion  
This section presents the comparative analysis of the proposed weighted radius approach and explains how the proposed method shows better results than other margin radius approaches. SVM is a famous and commonly used classification technique because it not only focuses on the separation of the data but also considers the maximum margin between two classes, as shown in Figure 3. Along with the maximum margin, the radius of the class is equally important to be considered to improve the overall performance of the algorithm. To achieve the objective, multiple margin radius-based techniques are proposed, and different radius bounds are presented. The recently proposed margin radius-based approach presented in [14] computes the radius using the mean of the class. The mean can shift to higher values. When the data contain some scattered points, then the radius of MEB will be higher, as shown in Figure 6. Because of the high radius value, the MEB of the class crosses the hyperplane and can also cover the area of another class in binary and multiclass problems. As a result, the samples in the intersected area of multiple MEBs can  
Figure 14: Intersected area of two MEBs using weighted mean. Figure 15: Intersected area of two MEBs using mean and weighted mean. be predicted incorrectly, as shown in Figure 15. To minimize the radius of MEB instead of the mean, the weighted mean of the class is computed. The samples targeted by the proposed methods are scattered points of one class. The proposed solution assigns weights to each class sample based on their distance from the mean value. The scattered samples always far from the mean as compared to the dense area's points. By assigning the weights to each instance, features are transformed from their original space to weighted feature space. The mean of weighted feature space, for instance, weighted mean, shifts to scattered points of the class. By moving the mean value from dense to scattered points, the radius of the class is decreased. As a result, the MEB of each class is minimized, as shown in Figure 7. Moreover, the area occupied by the proposed MEB is also reduced, and intersected area of multiclass is also decreased. Two-step training of WR-SVM is based on the computation of classification parameters and radius of MEB. After the training phase, when the samples are tested, the SVM classifier is used to classify the sample, and then the radius is used to check whether the sample is within MEB or not. The proposed model compared with F-SVM in terms of the true predicted number of samples for Linear and kernelized feature space as shown in Table 4. The existing techniques used kernelized feature space to compute the radius of MEB. In contrast, our proposed method considered the weighted kernelized feature space to minimize the value of radius for enhancing the efficiency of conventional SVM. In the first step, the kernel matrix transforms the feature space into kernelized feature space. After that, the weights are assigned to each sample based on its distance from the mean of the class. The MEB computed from the weighted kernelized feature space is reduced as compared to kernelized feature space. It is clear from Table 4 that the number of true predicted samples of the proposed model is higher than F-SVM. The proposed model's results on a ten-dimensional and non-linear dataset, i.e., synthetic dataset, in terms of accuracy is better than conventional SVM and margin radius-based F-SVM. The data contain some scattered points along with dense area, so the mean computed from kernelized feature space is shifted to a dense class area. The average accuracy achieved by Linear SVM on the synthetic dataset is 83.85, while the proposed model shows 90.86. Similarly, on two-dimensional datasets, including Sonar, Musk, and Breast cancer data, the samples move from MEB of F-SVM to proposed MEB because the true predicted samples rate is higher.