## 3 Our Model
### Construction of the Decision Coefficient Matrix  
The decision coefficient matrix is an important component for feature selection. To fully consider the impact of information from two different material views on individual thermal comfort, a method to construct a decision coefficient matrix under the joint view \(\varphi_{v_{1}v_{2}}\) is proposed in this study by comparing the important feature options under the two views. When calculating the membership function using the joint view, the maximum membership function value matrix of each feature of the training sample under each independent view \(\mu_{v_{1}}\) and \(\mu_{v_{2}}\) is obtained. In the feature domain, the effective feature that contributes the most to information decision-making in each sample under two single views is obtained. We consider the corresponding relationship between the effective training features under two views; that is, when a certain feature is an effective feature for the two different materials, we believe that the feature is effective in improving the classification performance of the model. Then, we choose to retain this feature; otherwise, we discard this feature. For example, from the view of two materials, the most effective feature of the same \(i\) sample is the \(j\)-dimensional feature, which means that the maximum membership function value of the \(j\)-dimensional feature (\(\mu_{v_{1}}(i,j)\), \(\mu_{v_{2}}(i,j)\)) is greater than other characteristics of the sample. Then, we can indicate that the \(j\)-dimensional feature is the most important feature in information decision-making under the two views, and we select this feature to construct the decision coefficient matrix. As a result, we not only retain the characteristic information of each sample under each single view but also integrate different characteristics of the two materials, thereby optimizing the final decision-making effect. As shown in Figure 2, the decision coefficient matrix under the first material view is \(\varphi_{v_{1}}\), and \(\varphi_{v_{1}}\) is the matrix based on \(\mu_{v_{1}}\). The values of the obtained matrix are 0 or 1. The feature with the maximum membership function value of each sample is marked as 1. The decision coefficient matrix \(\varphi_{v_{2}}\) of the second material view is constructed in a similar manner. Therefore, we combine the feature selection matrix under the joint view \(\mathbf{\varphi}_{v_{1}v_{2}}\), which is expressed as:  
\[\mathbf{\varphi}_{v_{1}v_{2}}= \begin{pmatrix}\mathbf{\varphi}_{v_{1}}(1,1)\otimes\mathbf{ \varphi}_{v_{2}}(1,1)&\dots&\mathbf{\varphi}_{v_{1}}(1,K)\otimes\mathbf{\varphi}_{v_{2 }}(1,K)\\ \vdots&\ddots&\vdots\\ \mathbf{\varphi}_{v_{1}}(D,1)\otimes\mathbf{\varphi}_{v_{2}}(D,1)&\dots&\mathbf{\varphi}_ {v_{1}}(D,K)\otimes\mathbf{\varphi}_{v_{2}}(D,K)\end{pmatrix} _{D\times K} \tag{10}\]  
where \(j\!=\!1,2,\dots,D\), \(k\!=\!1,2,\dots,K\), \(K\) is the number of rules, and \(\mathbf{\varphi}_{v_{1}v_{2}}\) is based on two single view decision coefficient matrices \(\mathbf{\varphi}_{v_{1}}\), \(\mathbf{\varphi}_{v_{2}}\). In other words, if the maximum value of the membership function is the same feature to form the corresponding matrix, then it is set to 1; otherwise, it is set to 0. In [59], the decision coefficient matrix \(\mathbf{\varphi}\) is a matrix for feature selection through random partitioning and random combination, ensuring the interpretability of the fuzzy rules in the hidden layer. In [60], information entropy is determined by the method of information gain from 0 to 1 for each matrix, each element is assigned as a random initial value, and then features are selected according to a certain ratio. These methods are all effective methods for a single dataset. This study fully considers the advantages of information supplementation and important features between the two views for the dual-view classification task. Therefore, the two-view decision coefficient matrix constructed in this study contains the characteristics of the source training samples from two views.