## 4 CSH-SVM Classification Algorithm
### Validation Results  
The multicategory support vector machine substituted each sample datum in the dataset into each hyperplane equation separately, and the classes considered as positive  
Figure 9: Schemes follow the same formatting.  
samples had significantly different results in their corresponding hyperplane equations. Taking the original hyperplane as an example, when solving the hyperplane equation \(OH_{1}\), the samples marked as the first category were regarded as positive samples, and the samples marked as other categories were regarded as negative samples. Thus, when the first category is substituted into the hyperplane equation \(OH_{1}\), the computed results should be positive and the computed results of the other categories should be negative. In order to verify the accuracy of the CSH-SVM improvement algorithm, we randomly selected 20 samples from each class in the tower dataset to form a validation dataset with a total of 80 samples, and Figure 10 shows the results of validating the four classes of samples in the corresponding hyperplane equation. This process simulates the model-building process of SVM in multiclassification scenarios, and the calculated values obtained through this process can intuitively compare the classification performance of SVM between scenarios. It can be found in Figure 10 that the resulting values obtained from the samples of the corresponding categories under the hyperplane equations of those categories were significantly different from those of the other categories.  
Using the CSH-SVM improvement algorithm, the resulting values obtained for the four classes of samples in the corresponding hyperplane equations are shown in Figure 11.  
Figure 10: Calculated results of four types of samples under the original hyperplane.  
From the data in Figure 11, it is clear that even though the calculation results of the improved hyperplane for the samples were different from those of the original hyperplane, the classification results are consistent. The calculation results of the first category of labeled data are clearly different from the other categories. By observation, it was found that the calculation results of the two hyperplanes had opposite trends with the samples. The calculated result values that were larger in the original hyperplane \(OH_{1}\) became smaller in the improved hyperplane \(OH_{23}\) instead. Thus, the images in the two figures, which have almost the same shape, change in the exact opposite trend.  
In order to purposefully compare the classification performance of the two hyperplanes for the transition area, two support vector machines were used to classify the actual operational data of the towers separately, and a total of 1500 test samples from the four types were selected out of 6000 samples. Among them, 77 samples were misclassified at least once. In order to make the images easy to observe, the samples that were correctly classified twice are ignored, and only the classification results of the 77 samples that were misclassified are plotted. Figures 12 and 13 show the classification results of the one-vs-all classification SVM and CSH-SVM, respectively.  
Figure 11: Calculation results of four types of samples under improved hyperplane.  
Figure 12: Classification results of misclassified samples in one-vs-all classification SVM.  
By comparison, the improved CSH-SVM algorithm had higher accuracy for the multi-classification problem, with 15 out of 77 samples correctly classified to the corresponding class with the new hyperplane. The quadratic planning prediction for these 15 samples shows that all such samples existed in the transition area. The classification accuracy was 94.87% before the improvement and 95.87% after the improvement. It is concluded that this improved algorithm has no effect on the sample points that exist in well-defined areas, whereas it can improve the classification accuracy for sample points existing in unspecified areas, making the results of the one-vs-all classification problem more accurate.