# Decision-Refillable-Based Shared Feature-Guided Fuzzy Classification for Personal Thermal Comfort
## 1 Introduction  
People spend approximately 90% of their time indoors [1]. Thermal comfort is one of the main factors affecting building energy and work performance [2]. The study of building comfort is beneficial for reducing building energy consumption and improving building environment satisfaction [3]. In the past few decades, many experts and scholars have conducted in-depth research on thermal comfort models using different methods [4, 5, 6, 7, 8]. For example, [PERSON] reviewed the most used thermal comfort models and indicators with their variants and discussedtheir usage in control problems referring to energy management in indoor applications [4]. [PERSON] et al. analyzed the adaptive models in ASHRAE Standard 55 [9], the European EN 15251 [10], the Dutch ISSO 74 [11] and the Chinese GB/T 50785 [12], then identified the main sources of uncertainty around the application of adaptive models, and analyzed the difference between the adaptive comfort models quantitatively [5]. [PERSON] et al. summarized the progress in the literature concerning the dynamic characteristics and comfort assessment of airflow in four main sections. It will hopefully aid the understanding of human thermal comfort perception of indoor airflow [6]. [PERSON] et al. studied the thermal comfort effects of asymmetric radial environments under different exposure durations, they proposed relational equations with consideration of exposure duration [7]. [PERSON] et al. transferred the developments in human metabolic science to the built environmental context. They led to the conclusion that the algorithm of human metabolism will affect thermal comfort modeling with precision [8]. These methods can be roughly classified into two categories. The approaches in the first category use statistical methods to study thermal comfort models. Among them, the predicted mean voting (PMV) model established by [PERSON] [13] and the adaptive thermal comfort model proposed by [PERSON] et al. [14] are the most representative. However, PMV does not have self-learning ability. Although the adaptive thermal comfort model proposed by [PERSON] et al. can self-learn and self-correct, its self-correction ability is limited. Its accuracy drops dramatically for new climate conditions or building types. In addition, the thermal comfort of building occupants may be affected by factors not included in these models (such as gender, age, health status [15; 16], and long-term and short-term adaptation [17; 18]). The approaches in the second category mainly use machine learning algorithms to study thermal comfort models. Many scholars have applied machine learning algorithms to thermal comfort research [19]. Although thermal comfort models based on machine learning algorithms outperform traditional thermal comfort models [20], the commonly used artificial neural network (ANN), support vector machine (SVM), and random forest (RF) all have poor semantic interpretability and, thus, have difficulty describing the importance of each input variable. Recently, some scholars have conducted research on thermal comfort from the perspective of fuzzy logic reasoning [21; 22]. For example, [PERSON] et al. presented a type-2 fuzzy method based on a data-driven strategy for the modeling and optimized the thermal comfort parameters. The proposed method can be used to realize a comfortable and energy-saving environment in a smart home or intelligent buildings [21]. [PERSON] et al. determined a new comfort index using fuzzy logic based on the responses of subjects, it can evaluate a new personalized ventilation system [22]. In terms of the input parameters, these models use the same environmental and personal factors as in the PMV model. or add new features, such as skin temperature [23] and time [24]; however, they do not consider usage scenarios or population features. Therefore, thermal comfort models face the following challenges:  
1.