# Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach
## 3 Results and Discussions
### Personalized Classification Models  
In order to develop online-personalized models, it is necessary to consider two main challenges, first the developed model should be able to handle the new, personal, data in the training set. Additionally, the developed model should be adapted to the new personal data without any bias to the majority of the old (non-personal) data. Different approaches are used to handle these challenges such as incremental learning methods [34], which work on adapting and retuning the parameters of the general model based on the newly collected data. Another approach is the localized learning, which is based on developing a local model for each test point or subset of the test set [35]. In the present paper, the KNN-LS-SVM localized learning approach is used because of its simplicity and efficiency. Two techniques were used to test the localized models, the first based on LOSO testing approach, and the second approach was based on leave-one-out (LOO) testing approach. 3.1 Developing Personalized Models Using the Selected 12 Features and Different Class-Configurations Based on LOSO Testing Approach  
As explained earlier, to develop a personalized classification model the new personal data were not considered in the training set to compare the performance with the global model. In other words, the new subject (the subject data that left out of the training set) is completely unknown to the model, which simulates the case when the model is dealing with an unknown test subject. The used localized learning approach of KNN-LS-SVM searches for the most similar (based on the similarity criterion, see (3)) training points to the new test point (from the new subject) in the input space by which a local model is developed to classify this test point. The resulted error performance (precision, sensitivity, F1-score, and accuracy) of the KNN-LS-SVM classifier based on LOSO testing approach and \(K=5\) is presented in Table 7. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Configurations** & **Classes** & **Precision** & **Sensitivity** & **F1-Score** & **Accuracy** \\ \hline \multirow{3}{*}{Conf. 1} & Class 1 & 0.53 & 0.37 & 0.44 & \\ \cline{2-6}  & Class 2 & 0.83 & 0.89 & 0.86 & 0.81 \\ \cline{2-6}  & Class 3 & 0.79 & 0.75 & 0.77 & \\ \hline \multirow{3}{*}{Conf. 2} & Class 1 & 0.88 & 0.88 & 0.88 & \\ \cline{2-6}  & Class 2 & 0.75 & 0.66 & 0.70 & 0.81 \\ \cline{2-6}  & Class 3 & 0.82 & 0.89 & 0.86 & \\ \hline \multirow{3}{*}{Conf. 3} & Class 1 & 0.88 & 0.88 & 0.88 & \\ \cline{2-6}  & Class 2 & 0.88 & 0.91 & 0.89 & 0.85 \\ \cline{1-1} \cline{2-6}  & Class 3 & 0.88 & 0.78 & 0.83 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The error performance (precision, sensitivity, F1-score, and accuracy) of general Least Squares Support Vector Machine (LS-SVM) model for the three different 3-class configurations. 3.2 Developing Personalized Models Using the Selected 12 Features and Different Class-Configurations Based on Leave-One-Out (LOO) Approach  
In contrast with the first approach, for each subject one data-point is tested and the rest of the same subject data-pointdata-points are integrated with the training data. This approach mimics online personalized streaming modelling, since the new streaming personal data is considered in the training dataset and a dedicated classifier is developed online for each new test data-point. The obtained error performance of the KNN-LS-SVM classifier based on LOO testing approach and \(K\) = 5 is depicted in Table 8.  
For the proposed personalized models, the first approach of LOSO is mimicking the case that the model is applied to an unknown subject to predict individual's thermal sensation level based on the measured variables. The localized model is searching for the most similar (nearest) training points to each test point, of this subject, that to train the classification model for each test point.