# Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study
## 3 Methods
### Data Preprocessing and Arrangement  
We observed varying elicited emotional expressions across subjects. For example, some individuals had no labels for certain emotion stimulus videos (e.g., no anger labels for anger video, no surprise label for surprise video, etc.), indicating insufficient facial expression stimulation. This disparity in label counts suggests that the threshold and manner of eliciting each emotion differ between individuals. The distribution of labels for each emotion across 9 stimulus videos and a neutral video for one demonstrative subject (no. 22) is shown in Table 1.  
Notably, there were no labels for anger, surprise, and disgust emotions in subject 22's respective video stimulus experiments. Additionally, the dataset contained only a few labels for anger and no labels for disgust at all, presenting challenges in achieving a balanced dataset for training and classification. To address this challenge, we created separate models for specific recognition tasks. To overcome the label imbalance, we collected all emotion labels specific to each discrete emotion from several video stimulus experiments into a single dataset for training each subject's model. To mitigate the impact of imbalanced label distribution, we carefully curated emotions with a substantial number of labels for accurate and unbiased classification results. For example, for subject no. 22, we trained a model to only recognize sadness, neutral, and happiness, which were the only well-represented labels for this subject. To ensure a robust analysis, we sampled a reduced balanced subset of 1600 instances for each of the emotions for ML model training.  
In contrast, for generic models, we consolidated data across all subjects, creating a \"one-size-fits-all\" dataset. This dataset was used to train generalized models that served as a baseline for comparison against the personalized models. The models were trained on combinations of emotions that were adequately represented across the dataset, providing a holistic view of emotion recognition across a varied population. We trained a separate model for each combination of emotions sufficiently represented by a subject (e.g., a one-size-fits-all model for happy vs. sad vs. neutral, a one-size-fits-all model for happy vs. neutral, etc.).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**ActualPrompt** & **Amusement** & **Anger** & **Awe** & **Disgust** & **Enthusiasm** & **Fear** & **Liking** & **Neutral** & **Sadness** & **Surprise** \\ \hline Anger & 0 & 0 & 0 & 0 & 0 & 3 & 0 & 0 & 0 & 0 \\ Disgust & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ Sadness & 398 & 213 & 135 & 227 & 87 & 111 & 128 & 1812 & 9 & 159 \\ Neutral & 6766 & 6752 & 6822 & 3339 & 7036 & 6993 & 6541 & 5468 & 7126 & 966 \\ Surprise & 0 & 37 & 0 & 0 & 0 & 116 & 1 & 0 & 0 & 0 \\ Happiness & 40 & 198 & 0 & 495 & 29 & 0 & 6 & 0 & 0 & 1832 \\ \hline \end{tabular}
\end{table}
Table 1: The discrepancy between the emotions evoked (actual) for one demonstrative subject vs. the emotional stimulus provided (prompt). We use the emotions evoked for our ground truth labels.  
We refer to these approaches as the \"personalized dataset\" method, where datasets are tailored to individual emotional profiles, and the \"generic dataset\" method, where data from multiple subjects are aggregated. This distinction is crucial for our study, as it highlights the differences in model training and effectiveness between personalized and generalized emotion recognition approaches.