## 4 Experiment and Discussion
### Performance Evaluation  
The learning algorithm proposed in this study is compared with the classical optimization algorithms (JADE-C, SADE-C, GFS-ADABOOST-C, and GFS-MAXLOGITBOOST-C) in the software. The membership function and the research method of constructing the decision coefficient matrix from the view of fusion were compared and verified.  
1. Classification performance and generalization performance According to Figure 7, the classification and generalization performance of TMV-TSK-FC on the above two datasets are observed. Based on the training structure of the first dataset, QI-TSK and GFS-ADABOOST-C have good training accuracies, and TMV-TSK-FC is able to perform classification as well as the other algorithms. Based on the training structure of the second dataset, which has a larger number of samples in the dataset, the use of TMV-TSK-FC with the fusion of the two material views improves the classification accuracy compared with the other algorithms. In addition, Figure 7 also shows that TMV-TSK-FC has good generalization performance. Since TMV-TSK-FC effectively integrates the feature information of two views and selects features relating closely to the information decision for output rule, it has better generalization performance. It is worth noting that the output of the TMV-TSK-FC training intermediate layer, the output of each fuzzy rule, and the output of the final fuzzy classifier are all interpretable.
2. Optimization method for feature selection Figure 8 shows the effect of TMV-TSK-FC on the classification and generalization performance before and after feature selection optimization. First, the membership function value of each feature under the optimized joint view is solved. The method used to obtain the solution takes the arithmetic mean of the membership function value of the feature from the two views based on the fuzzy center. From the view of classification accuracy, the optimized method indeed provides TMV-TSK-FC with a better classification ability. This advantage may be due to the selection of dominant features that have a large contribution to decision-making under both views, which improves the performance of classification and generalization.  
Figure 7: Comparison between TMV-TSK-FC and the other algorithms.  
3. The optimization method of the decision coefficient matrix  
To further verify the rationality of the proposed method with respect to the construction of the decision coefficient matrix, a method of calculating the arithmetic mean of the decision coefficient matrix under two single views is used in this study as a comparison method. The information gain method determines the information entropy, assigns a random initial value from 0 to 1 to each element, and then randomly selects features according to a ratio of 70%. The purpose of this study is to observe the impact of the above two optimization methods on the classification and generalization performance. According to Figure 9, the classification and generalization performance of TMV-TSK-FC by constructing the decision coefficient matrix optimized in this paper is generally higher than that of the average decision coefficient matrix. The experimental results show that the method of optimizing the decision coefficient matrix proposed in this paper is reasonable. Therefore, the decision coefficient matrix can be applied to retain the feature information that contributes significantly to the decision under two views, by which means the inherent information of the two independent views can be fully considered by the final decision. In addition, the learning efficiency, classification, and generalization performance of the fuzzy classifier can be improved.