## 4 Case Study
### Results and Discussion
#### 4.2.2 Cognitive Emotion Recognition  
Some experimental results of the proposed method are shown in Figure 8 and Table 2. They clearly show that the F1-scores of the proposed method for the four cognitive emotions increased with an increase in the number of selected samples. This indicates that the proposed method captured higher quality samples when the total number of training samples was the same. Even when the number of selected samples was 170, the F1-score for each kind of cognitive emotion was more than 90%. The method also showed good stability: the F1-score for each kind of cognitive emotion was more than 80%, even when the number of selected samples was relatively small. The results, therefore, indicate that the proposed method can accurately and stably identify cognitive emotional states in a small sample and with a low cost of labeling and training. The confusion matrix with the best average F1-score (91.99%) for the classifier for the four cognitive emotions is shown in Figure 9. First, the distribution of the training sets captured by the proposed active learning-based data optimization algorithm was inversely deduced: the number of samples labeled \"frustration\" was 53, the number labeled \"boredom\" was 38, \"doubt\" was 40, and \"pleasure\" was 49. Based on the distribution of the entire dataset, samples labeled \"frustration\" and \"pleasure\" were more frequently captured in the training set. This result indicates that the feature vectors for these two cognitive emotions did not have a discrimination ability as high as that of the feature vectors for \"boredom\" and \"doubt\". However, Figure 7 shows that \"boredom\" and \"doubt\" were the two emotions with relatively low F1-scores. This seems to be a common phenomenon in sample selections performed by active learning algorithms, which achieve synchronous  
Figure 8: The relationship between the performance of the proposed method and the number of samples selected through active learning. improvements in recognition accuracy for all classes by suppressing the number of easily recognizable samples. In the end, the recognition accuracy of the class that is considered difficult to classify by active learning algorithms will be higher than that of other classes. The advantage of this is that it will not cause the accuracy of certain classes to become higher and higher while the accuracy of some classes becomes lower and lower, ensuring a stable improvement in the overall recognition accuracy (average F1-score). In addition, the F1-scores for \"frustration\" had the highest value of 94.36%, which may be related to the large number of \"frustration\" samples in the training set. It can also be observed from Figure 9 that there were relatively more samples that were misclassified between \"boredom\" and \"doubt\", which affected their precision and recall. Moreover, Table 2 also shows that the F1-scores for \"boredom\" and \"doubt\" were consistently lower than those of \"frustration\" and \"pleasure\", which may be due to the relatively small number of samples labeled \"boredom\" and \"doubt\". But, if the number of training samples for the four cognitive emotions was the same, the recognition accuracy of \"boredom\" and \"doubt\" were not always the lowest. In this study, some competing methods with excellent performance were compared with the proposed method. The performance of the competing methods and proposed method are shown in Table 3. The proposed method outperformed the competing methods on the MMI Database, specifically manifesting in a higher accuracy, lower labeling costs, higher quality samples, and greater adaptability to small sample sizes. Firstly, it is worth