## 4 Experimental Results and Analysis
### Analysis of Experimental Results
#### 4.3.4 Page-Blocks Data Set  
The values of the fitness functions for different categories on the page-blocks dataset are shown in Table 14, so the final classification order is 3, 5, 4, 2, and 1. The threshold values of MTS1-MTS4 are 6.1112, 5.3295, 1.6599, and 7.5448, respectively, and the feature selection results of the joint optimization model in this paper are shown in Table 15. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Parameter Set**} & \multicolumn{5}{c}{**Evaluation Index**} \\ \cline{3-8}  & & **Accuracy** & **Macro-F1** & \multicolumn{2}{c}{**Kappa Coefficient**} & **CBA** & **Cost** \\ \hline Optimized & joint optimization model & 0.6976 & 0.4023 & 0.5841 & 0.4479 & 22.6081 \\ BT-MTS & Use SNRLB gain value to & 0.4523 & 0.3687 & 0.3065 & 0.3157 & 108.3620 \\ RF & Ntree = 100, & 0.7857 & 0.6536 & 0.7025 & 0.5972 & 39.3054 \\ FCBF-SVM & BoxConstraint = 1.0625, & 0.5952 & 0.3306 & 0.4251 & 0.2822 & 61.1883 \\ SMOTE-KNN & K = 6 & 0.6428 & 0.5255 & 0.5007 & 0.4979 & 50.3315 \\ BTNN & Hiddensize = 5 & 0.5813 & 0.3396 & 0.4068 & 0.1629 & 63.1848 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparison of the classification results of the glass data set of the glass data set. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(c_{1}\) & \(c_{2}\) & \(c_{3}\) & \(c_{4}\) & \(c_{5}\) \\ \hline Stage1 & \(-45.4349\) & \(-365.7163\) & \(-123,953.3926\) & \(-4116.0841\) & \(-40,928.4861\) \\ Stage2 & \(-26.5124\) & 841.9145 & / & 2696.5264 & \(-1993.7109\) \\ Stage3 & \(-1211.3206\) & 462.6110 & / & \(-4297.0959\) & / \\ Stage4 & \(-193.7301\) & \(-360.7507\) & / & / & / \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Values of fitness function at different stages of the Page-blocks data set. Figure 6: Comparison of DRR of the glass data set. The classification order of the BT-MTS method is category 1, 4, 2, 3, and 5, and the threshold values are 8.7889, 4.0251, 3.5549, and 3.4798, respectively. The feature subsets obtained according to the Taguchi\({}^{\prime}\)s method are shown in Table 16, and the DRR of the two MTS methods are shown in Figure 7.  
The Page-blocks data set comparison results of the six methods are presented in Table 17, and the feature selection result of FCBF-SVM is \(x_{4},x_{8}\). As it can be observed from Table 17, the accuracy, macro-F1, kappa coefficient, CBA, and total misclassification cost of optimized BT-MTS are better than those of BT-MTS. In addition, the DRR of the method proposed in this paper is also higher, which also indicates that the optimized BT-MTS not only improves the classification accuracy but also enhances the classification efficiency, and the ability of MTS to solve the imbalanced multi-classification problem is enhanced. Compared with the FCBF-SVM method, the optimized BT-MTS did not have a clear advantage regarding the average DRR. However, in terms of imbalanced multi-classification performance, the optimized BT-MTS was substantially superior to the FCBF-SVM. This may be due to the fact that the FCBF-SVM method loses some important information in the process of feature selection. Compared with the methods of RF, SMOTE-KNN, and BPNN, the classification performance of the optimized BT-MTS and RF is relatively better from the comprehensive view of the five evaluation indexes. Although the accuracy, macro-F1, kappa coefficient, and CBA of the SMOTE-KNN are \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \(x_{1}\) & \(x_{2}\) & \(x_{3}\) & \(x_{4}\) & \(x_{5}\) & \(x_{6}\) & \(x_{7}\) & \(x_{8}\) & \(x_{9}\) & \(x_{10}\) \\ \hline MTS1 & 1 & 0 & 0 & 1 & 1 & 1 & 1 & 0 & 0 & 0 \\ MTS2 & 1 & 1 & 0 & 1 & 1 & 1 & 0 & 0 & 0 & 1 \\ MTS3 & 1 & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 0 & 0 \\ MTS4 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 \\ \hline \hline \end{tabular} Note. “1”means using the variable, “0” means not using the variable.  
\end{table} 
Table 16: Feature variable selection results of BT-MTS of the Page-blocks data set.