## 2 Methodology
### Development of Thermal Models  
The development of thermal models follows the typical machine learning pipeline, as shown in Figure 5. In this study, three different feature sets were used for developing the thermal models. The base feature set consists of indoor air temperature, indoor relative humidity and side face skin temperature. Since clothing plays an important role in thermal comfort, clothing surface temperature was selected as additional feature to construct feature set A. Moreover, since thermal comfort is also affected by temperature gradient, particularly in the transient conditions, side face skin temperature difference between two consecutive measurements was also selected as an additional feature to construct feature set B. As for the baseline, PMV was used to compare with the two classification models. However, several assumptions were set when calculating PMV. Firstly, since the mean radiant temperature (MRT) and indoor air velocity were not measured and the experimental environment avoided direct radiation,  
Figure 5: The development pipeline of thermal models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multirow{2}{*}{**Start Time**} & \multirow{2}{*}{**Transition Time**} & \multirow{2}{*}{**End Time**} & \multicolumn{2}{c}{**Start**} & \multicolumn{2}{c}{**Transitional**} & \multicolumn{1}{c}{**End**} \\  & & & & **Temperature** & **Temperature** & **Temperature** \\  & & & & (\({}^{\circ}\)C) & (\({}^{\circ}\)C) & (\({}^{\circ}\)C) \\ \hline \multirow{3}{*}{3/11} & 9:50 & 11:00 & 11:50 & 17.4 & 29.3 & 22.3 \\  & 13:45 & 15:20 & 16:45 & 22.6 & 29 & 24.3 \\  & 19:30 & 21:45 & 22:30 & 19.3 & 28.5 & 20.9 \\ \hline \multirow{3}{*}{3/12} & 10:10 & 11:10 & 11:30 & 17.7 & 29.5 & 25.3 \\  & 13:40 & 14:55 & 15:30 & 21.2 & 28.4 & 24.4 \\ \hline \multirow{3}{*}{3/13} & 9:35 & 11:20 & 12:00 & 19 & 28.9 & 24.1 \\  & 13:40 & & 17:30 & 29.9 & & 18.5 \\  & 19:40 & 21:05 & 22:35 & 21.2 & 28.5 & 24.3 \\ \hline \multirow{3}{*}{3/14} & 9:30 & 10:50 & 11:35 & 20.8 & 29 & 26.5 \\  & 13:35 & 16:50 & 17:35 & 17.3 & 29.3 & 23.8 \\  & 20:00 & 20:45 & 22:30 & 20.9 & 28 & 18.7 \\ \hline \multirow{3}{*}{3/15} & 9:35 & 11:05 & 11:40 & 19.5 & 29.5 & 24 \\  & 13:30 & 14:50 & 16:30 & 21.5 & 30 & 18.1 \\ \hline \multirow{3}{*}{3/16} & 8:50 & 9:00 & 10:20 & 16.1 & 14.4 & 26.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Temperature changes during each session. MRT was approximated with indoor air temperature when calculating PMV and the indoor air velocity was calculated as 0.1 m/s since there was no draft risk. In addition, since the subjects were seated during the whole study, the metabolic rate was calculated as 1 met and there was no external work. The whole dataset was split into two parts where 80% of data is the training set and 20% is the testing data. In addition, before training the best model, the hyper-parameters used in the best models were selected with 5-fold cross-validation, as shown in Table 4. Cross-validation is one of the common methods of model selection, which aims to find the model that gives the best prediction. The models fit on the training set is used to predict the test set. Usually, many such splits are used, and the results are averaged over splits [29]. In terms of classification algorithms, rather than neural network, SVM and RF were implemented for classification since they can perform well with a relatively small-scale dataset. SVM can be used to develop supervised classification models with high-dimensional and non-linear data. Since the data is unlikely to be separated linearly, with SVM classification, soft margin usually performs better than hard margin [30]. Generally, penalty parameter of C is used to control how soft of the margin can be selected with cross-validation. The Equations (1)-(3) show the optimization framework in SVM. Moreover, compared to quadratic programming to solve the optimization problem, kernelized SVM can be computed much more efficiently.