# Decision-Refillable-Based Shared Feature-Guided Fuzzy Classification for Personal Thermal Comfort
## 1 Introduction
Test scenarios (climate, building type, and user population) and feature dimensions: Researchers have confirmed that the season, climate, and building type impact human thermal adaptability and thermal expectations [25]. Most of the recent research focuses on the Mediterranean climate region [26; 27]. In addition, due to inconsistent observation indicators for subjects in different research or scenarios, the feature dimensions of the datasets are usually inconsistent, and previous thermal comfort models are not suitable for datasets with multiple different feature dimensions. Therefore, for different climatic regions, it is necessary to develop thermal comfort models that can be applied to multiple datasets of different feature dimensions to enable a prediction using personal thermal comfort models in a wider range of scenarios. 2. Research on the shared features of specific populations: For a particular type of building, the long-term user population generally has the same or similar shared features, such as age, activity level, clothing level [28], and human behavior [29]. This kind of specific observation population has similarities and correlationsregarding the impact of shared features on the final decision's results. Therefore, it is particularly important to identify shared features that are relatively easy to observe and greatly impact the final decision classification among these features. However, the recent research on personal thermal comfort models has not addressed the impact of shared features of specific populations on comfort. Semantic interpretability of thermal comfort models:  
The choice of algorithm is one of the main factors affecting the accuracy of machine learning-based thermal comfort models [30]. According to previous studies of thermal comfort models, many machine learning-based models have difficulty expressing the relationship between input variables and model outputs; hence, these models are called \"black box models\". Although these algorithms can achieve a good performance in the learning process, they do not have good scientifically derived semantic interpretability. To address these issues, the following steps are performed in this study: (1) extracting key shared features that greatly impact classification performance from the features of specific scenarios and specific populations and highlighting the guiding role of these key shared features in the performance of the final model; (2) strengthening the guiding role of the shared features of a specific population in the classification while considering the compensatory effect of extra features on human thermal comfort; and (3) constructing a highly interpretable fuzzy classifier to mine the effect of the thermal environment on human thermal comfort; thereby, better guiding building environmental design. The features of a specific population can be roughly classified into shared features and extra features. The shared features do not change with external conditions and are easily acquired using conventional acquisition methods. The extra features may change with changes in the external environment or have feature information that is difficult to acquire. Therefore, in the modelling process, it is necessary to strengthen the guiding role of shared features in the final decision, to fully consider the impact of extra features on the classification, and to ensure that the antecedent parameters of each rule, the output of each rule, and the outputs of the model are all interpretable; thereby, making the thermal comfort model applicable to more usage scenarios.