## 4 Experiment and Discussion
### Description of the Comparison Algorithm  
In this study, we used the four classical optimization algorithms with DBN [61], QI-TSK [60], and the KEEL software (KEEL can be downloaded from http:www.keel.es/download.php (accessed on 30 March 2022)), as comparison algorithms to evaluate and verify the rationality and superiority of the proposed model. Knowledge extraction based on evolutionary learning (KEEL) is a machine learning software based on Java that integrates a variety of classical algorithms and their corresponding optimization algorithms. It mainly includes classification algorithms, regression algorithms, and semi-supervised learning algorithms. The four classical optimization algorithms adopted are JADE-C, SADE-C, GFS-ADABOOST-C, and GFS-MAXLOGITBOOST-C. Among the above algorithms, DBN is the classic nonfuzzy machine learning algorithm, QI-TSK is the optimized TSK algorithm, JADE-C and SADE-C are the classic nonfuzzy algorithms in the KEEL software, and GFS-ADABOOST-C and GFS-MAXLOGITBOOST are the classic nonfuzzy algorithms in the KEEL software. Therefore, the performance of the proposed model, TMV-TSK-FC, can be comprehensively observed by comparison with the classical DBN algorithm, the optimized TSK algorithm, and the classical fuzzy and nonfuzzy algorithms in the KEEL software. The detailed algorithm descriptions are presented in Table 4. In addition, comparative experiments were conducted in this study to investigate the rationality of optimization measures such as the construction of membership functions and the construction of the decision coefficient matrix from the fusion view.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithms** & **Main Descriptions of Compared Algorithms** & **Main Descriptions of TMV-TSK-FC** \\ \hline \multirow{3}{*}{DBN} & (1) Hierarchical structure with multiple hidden layers; only the nodes of adjacent layers are connected. & (1) Dataset input: bi-view information input. \\  & (2) The process of feature learning has better feature expression. & (1) The basic building blocks of QI-TSK-fc (td \(>\) 1) are all composed of optimized zero-order TSK fuzzy classifiers. Each base building unit is aligned with the adjacent base building unit. & (2) Feature selection and rule output: By constructing a membership function from a joint view, comparing the size of its membership value, select the membership function value that contributes the most to decision making, so that the feature information of the two views can be effectively processed. Fusion, and by constructing a decision coefficient matrix, select the features that are closely related to information decision making for rule output. & (1) Dataset input: bi-view information input. \\  & (2) The process of feature learning has better feature expression. & (2) Feature selection and rule output: By constructing a membership function from a joint view, comparing the size of its membership value, select the membership function value that contributes the most to decision making, so that the feature information of the two views can be effectively processed. Fusion, and by constructing a decision coefficient matrix, select the features that are closely related to information decision making for rule output. & (3) Fuzzy rules: TMV-TSK-FC has high usability and interpretability. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Algorithm descriptions.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Parameter** & **Value** \\ \hline Number of fuzzy rules \(K\) & 55–60 \\ Number of cluster centers \(C_{1}C_{2}\) & 7 \\ \(\lambda\) & (0, 0.1) \\ \(\eta\) & (0, 0.1) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental parameter settings.