## 3 Results and Discussion
### Classification of Emotions  
When the optimal model orders and parameters for each labelled event were determined, they were used as features in a different ML classifier. The accuracies achieved per participant and ML algorithm are given in Table 3. The last column is the classification accuracy that would be achieved by a random classifier. The SVM algorithm was over all participants the best-performing algorithm and performed for all participants better than the random classifier (Table 3). More information about the classification performance of the SVM classifier running on the model orders and parameters as features was aimed for via confusion matrices. For most participants, the rather high accuracies were caused by a class imbalance of the labels added by the participants. For participant P13 (Figure 7), for example, classifying 76.5% of the events as 'happiness', led to an accuracy that was more than double the accuracy of the theoretical random classifier (38.1% versus 16.7%). Few other participants, such as participant P25 (Figure 8), had a confusion matrix in which the elements were more spread over the different emotions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Participant** & **KNN** & **SVM** & **Decision Tree** &
\begin{tabular}{c} **Random** \\ **Classifier** \\ \end{tabular} \\ \hline P1 & 60.0\% & 60.0\% & 40.0\% & 50.0\% \\ P2 & 30.2\% & 30.2\% & 30.2\% & 12.5\% \\ P5 & 54.3\% & 57.1\% & 65.7\% & 20.0\% \\ P6 & 50.0\% & 50.0\% & 42.9\% & 16.7\% \\ P7 & 57.1\% & 57.1\% & 42.9\% & 50.0\% \\ P8 & 56.7\% & 56.7\% & 56.7\% & 20.0\% \\ P10 & 66.7\% & 66.7\% & 33.3\% & 50.0\% \\ P11 & 44.0\% & 42.8\% & 37.4\% & 11.1\% \\ P13 & 33.3\% & 38.1\% & 19.0\% & 16.7\% \\ P25 & 54.5\% & 63.6\% & 27.3\% & 33.3\% \\ P26 & 40.0\% & 40.0\% & 26.7\% & 33.3\% \\ \hline Average & 49.7\% & 51.1\% & 38.4\% & 28.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracies achieved by the DBM-based classification with ML validation for every participant (P1–P26). The second to fourth columns are the average accuracies for the DBM classification with KNN, SVM and decision tree validation, respectively. The last row includes the accuracies that would have been achieved by a random classifier. We concluded that, despite the fact that the accuracies of the SVM algorithm are (sometimes much) higher than those of a random classifier (51.1% on average), the classification performance is often not high. Due to class imbalances of the labels, the classifier often  
Figure 8: Confusion matrix of the classification of participant P25. The vertical axis represents the true class of the labels added by the participant. The horizontal axis represents the labels that were predicted by the classification algorithm. Each number, so every colour, indicates how many times each true class was predicted as each emotion on the horizontal axis. Figure 7: Confusion matrix of the classification of participant P13. The vertical axis represents the true class of the labels added by the participant. The horizontal axis represents the labels that were predicted by the classification algorithm. Each number, so every colour, indicates how many times each true class was predicted as each emotion on the horizontal axis. predicts all events of one participant to be the same one or just two particular emotions. Another problem is the inherent subjectivity of the emotion labels. There is no way in which a researcher can assess whether an added label is correct or not. Even if subjects try their very best to label their experiences as accurately as possible, sometimes they just do not know how they feel, or whether this feeling is strong enough to be labelled. Moreover, there is not a large number of labels available. Emotion labelling can be confrontational and time-intensive, so it is hard to gather more labels per individual. Lastly, emotions are for one individual highly variable at different moments in time and in different circumstances.