# Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach
## 3 Results and Discussions
### Class Reduction  
From the confusion matrix in Table 5, it can be seen that the confusion is mostly observed between the adjacent classes. The main reason of such interclass confusion is that the features are not able to discriminate completely between these adjacent classes. For instance, the actual neutral class (0) is confused with 8.45% and 18.32% with slightly cool (-1) and slightly warm (1) classes, respectively. Hence, it is more convenient to reduce the seven thermal sensation classes into three classes representing thermal comfort (comfortable, uncomfortably cool, and uncomfortably warm). The class reduction is done based on three criteria, namely, maximum confusion, acceptable class imbalance, and avoiding overlap between classes. As mentioned earlier, the maximum confusion is observed between the adjacent classes (see Table 5). However, it is not possible to merge all adjacent confused classes due to the overlap. For example, the Slightly-Warm class is confused with the Neutral class by 22.9%, on the other hand, the Warm class is confused with the Slightly-warm by 31.96%. Hence, in order to merge the Slightly-warm class with the Neutral it should not be merged with Warm and vice versa. Therefore, merging must avoid any overlap between different classes. Another criterion is the class imbalance, as shown in Figure 7a and Table 5, where Cold is not recognized by the classifier due to the relatively very low number of instances labeled as Cold compared to the other classes. For an acceptable class imbalance, it is meant to consider the already existing class imbalance between the whole states that the frequency of a state occurrence is reducing by moving far from the Neutral state, as shown in Figure 7a. Figure 6: Error performance of the developed general classification model with the selected 12 input features (reduced dimension input space) for 7-classes classification problem. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & \multicolumn{4}{c}{**Actual Label (\(i\))**} \\ \hline  & & **Cold \%** & **Cool \%** & **Slightly Cool \%** & **Neutral \%** & **Slightly Warm\%** & **Warm \%** & **Hot \%** \\ \hline \multirow{4}{*}{**Cold**} & Else & 0 & 4.79 & 6.65 & 0 & 10.40 & 16.03 & 0 \\ \cline{2-10}  & Cold & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \cline{2-10}  & Cool & 33.20 & 33.33 & 0 & 0 & 0 & 0 & 0 \\ \cline{2-10}  & Slightly cool & 66.80 & 57.08 & 66.69 & 8.45 & 2.10 & 0 & 0 \\ \cline{2-10}  & Neutral & 0 & 4.79 & 26.66 & 71.81 & 22.90 & 4.03 & 0 \\ \cline{2-10}  & Slightly Warm & 0 & 0 & 0 & 18.32 & 56.25 & 31.96 & 6.71 \\ \cline{2-10}  & Warm & 0 & 0 & 0 & 1.42 & 8.35 & 35.99 & 46.65 \\ \cline{2-10}  & Hot & 0 & 0 & 0 & 0 & 0 & 12.00 & 46.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The normalized confusion matrix of Model II. Finally, it is necessary to avoid any overlap between the reduced classes by assigning each state to only one class. As there are different possibilities to obtain the new three classes, it is found that three configurations are the closest to the thermal comfort levels, considering the earlier mentioned criteria. Based on these criteria the seven classes were reduced into three classes with three different configurations as follows:  
* **Configuration 1** Merging the states of Cold (\(-3\)) and Cool (\(-2\)) into 'Class 1' (27 instances), merging Slightly cool (\(-1\)), Neutral (\(0\)), and Slightly warm (\(1\)) into 'Class 2' (149 instances), and merging Warm (\(2\)) and Hot (\(3\)) into 'Class 3' (40 instances) (Figure 7b). * **Configuration 2** Merging the states of Cold, Cool and Slightly-cool into 'Class 1' (57 instances), Neutral as 'Class 2' (71), and merging Slightly-warm, warm and Hot into 'Class 3' (88 instances) (Figure 7c). * **Configuration 3** Merging the states of Cold, Cool and Slightly-cool into 'Class 1' (57 instances), merging Neutral, and Slightly-warm into 'Class 2' (119 instances), and merging warm and Hot into 'Class 3' (40 instances) (Figure 7d).