# Decision-Refillable-Based Shared Feature-Guided Fuzzy Classification for Personal Thermal Comfort
## 3 The Proposed Fuzzy Classifier: SFG-TFC
### Algorithms  
This section presents the SFG-TFC algorithm. The specific training steps are specified in Algorithm 1.  
```
Input: The training set \(\mathbf{X}_{1},\ldots,\mathbf{X}_{M-1},\mathbf{X}_{M}\),where,\(\mathbf{X}_{i}=\left[\dot{x}_{1}^{i},x_{2}^{i},\ldots,\dot{x}_{N_{i}}^{i} \right]^{T}\) The corresponding class label set \(\mathbf{T}_{1},\ldots,\mathbf{T}_{M-1},\mathbf{T}_{M}\), where, \(\mathbf{T}_{i}=\left[\dot{t}_{1}^{i},\dot{t}_{2}^{i},\ldots,\dot{t}_{N_{i}}^{ i}\right]^{T}\) Output weight parameter \(\theta\), rule number \(K\) Step1: Acquisition and processing of shared features Step 1(a): the key shared feature dataset \(\mathbf{X}^{i}\) is extracted by the method of information gain Step 1(b): Perform sTSK training on \(\mathbf{X}^{i}\) Step 1(c): Calculation rule output matrix \(\mathbf{H}^{s}=\left(\begin{array}{c}\mathbf{H}_{1}^{s}\\ \mathbf{H}_{2}^{s}\\ \ldots\\ \mathbf{H}_{M-1}^{s}\end{array}\right)\) Step2: Re-projection of key shared features Step 2(a): Conduct TSK base unit training for additional features \(\mathbf{X}_{1}^{c},\ldots,\mathbf{X}_{M-1}^{c}\) to obtain the rule output matrix of additional features \(\mathbf{H}_{1}^{s},\ldots,\mathbf{H}_{M-1}^{s}\). Step 2(b): Project the corresponding relevant information in the rule output matrix \(\mathbf{H}^{s}\) of the shared feature to the corresponding base training unit again. Calculate the rule output matrix of each base training unit \(\mathbf{H}_{i}=\left[\begin{array}{c}\mathbf{H}_{i}^{s}:\mathbf{H}_{i}^{s} \\ \vdots\\ \end{array}\right]\) Step 2(c): Calculate the output weight of \(M\)-1 training base units \(\mathbf{\beta}_{i}\) Step 3: Integration of output weights Step 3(a): Generate the \(M\)-th rule output matrix \(\mathbf{H}_{M}\) Step 3(b): Optimize output weight \(\mathbf{\beta}_{M}=\mathbf{\beta}_{M}+\sum\limits_{i=1}^{M-1}\frac{\theta}{M-1 }\mathbf{\beta}_{i}\) Output: The prediction function of SFG-TFC: \(\mathbf{Y}_{M}=\mathbf{H}_{M}\times\mathbf{\beta}_{M}\)
```  
**Algorithm 1**Training Algorithm of SFG-TFC  
**Remark 1**.: _The datasets are collected at different time spans, and the features still have similarities or differences over time. The associations between similar features (shared features) and different features (extra features) are clarified to characterize their impact on the final personal comfort._  
**Remark 2**.: _The reasons for the enhanced classification performance of Algorithm 1 are analyzed from the perspective of feature enhancement and model optimization. First, this study analyzes the dataset features and classifies the features of each dataset into shared features and extra features. Then, the shared features play a guiding role, the extra features have a compensation effect, and the integrated output weight guarantees the classification performance of the model._  
**Remark 3**.: _The output weight parameter \(\theta\) and the number of rules \(K\) are important parameters that affect the performance of Algorithm 1. In repeated experiments in this study, the output weight parameter is 0.005, and the number of rules is 10-20._