## 4 Results and Analysis
### Result of DEAP Dataset
#### 4.3.2 Results of Embedded-Based Feature Selection on the DEAP Dataset  
Table 7 presents the experimental outputs obtained from embedded-based feature selection using LASSO on the DEAP dataset. This method selected the 34 best features for the classification task. The results indicate varied performances across different models when LASSO was applied. \begin{table}
\begin{tabular}{c c c c c} \hline
**No. of Features** & **Model** & **Labels** & **Accuracy (\%)** & **F1 Score (\%)** \\ \hline \multirow{8}{*}{160} & RF & Valence & 66.13 & 65.77 \\ \cline{2-5}  & \multirow{2}{*}{Logistic Regression} & Arousal & 60.34 & 58.08 \\ \cline{2-5}  & \multirow{2}{*}{Logistic Regression} & Valence & 60.90 & 60.20 \\ \cline{2-5}  & \multirow{2}{*}{XGBoost} & Arousal & 63.18 & 57.90 \\ \cline{2-5}  & \multirow{2}{*}{XGBoost} & Valence & 63.06 & 62.67 \\ \cline{2-5}  & \multirow{2}{*}{SVM} & Arousal & 60.68 & 58.49 \\ \cline{2-5}  & \multirow{2}{*}{SVM} & Valence & 61.59 & 60.67 \\ \cline{2-5}  & \multirow{2}{*}{RF} & Arousal & 63.06 & 57.08 \\ \cline{2-5}  & \multirow{2}{*}{RF} & Valence & **68.29** & **67.83** \\ \cline{2-5}  & \multirow{2}{*}{Logistic Regression} & Arousal & **60.90** & **59.09** \\ \cline{2-5}  & \multirow{2}{*}{Logistic Regression} & Valence & 53.86 & 50.43 \\ \cline{2-5}  & \multirow{2}{*}{XGBoost} & Arousal & 41.70 & 29.43 \\ \cline{2-5}  & \multirow{2}{*}{XGBoost} & Valence & **65.22** & **64.93** \\ \cline{2-5}  & \multirow{2}{*}{SVM} & Arousal & **62.04** & **59.94** \\ \cline{2-5}  & \multirow{2}{*}{SVM} & Valence & **61.67** & **60.74** \\ \cline{2-5}  & \multirow{2}{*}{RF} & Arousal & 61.90 & **59.98** \\ \hline \multirow{8}{*}{100} & RF & Valence & **66.59** & **66.21** \\ \cline{2-5}  & \multirow{2}{*}{Logistic Regression} & Arousal & **62.38** & **60.07** \\ \cline{2-5}  & \multirow{2}{*}{Logistic Regression} & Valence & **65.00** & **64.65** \\ \cline{2-5}  & \multirow{2}{*}{XGBoost} & Arousal & 48.06 & **32.26** \\ \cline{2-5}  & \multirow{2}{*}{XGBoost} & Valence & **65.79** & **65.42** \\ \cline{2-5}  & \multirow{2}{*}{SVM} & Arousal & **63.18** & **61.26** \\ \cline{2-5}  & \multirow{2}{*}{SVM} & Valence & **62.22** & **61.91** \\ \cline{2-5}  & \multirow{2}{*}{RF} & Arousal & **62.48** & **61.08** \\ \cline{2-5}  & \multirow{2}{*}{RF} & Valence & **66.36** & **65.90** \\ \cline{2-5}  & \multirow{2}{*}{Logistic Regression} & Arousal & **62.72** & **60.69** \\ \cline{2-5}  & \multirow{2}{*}{Regression} & Valence & 59.31 & 58.27 \\ \cline{2-5}  & \multirow{2}{*}{XGBoost} & Arousal & 41.70 & **29.43** \\ \cline{2-5}  & \multirow{2}{*}{XGBoost} & Valence & **65.34** & **64.77** \\ \cline{2-5}  & \multirow{2}{*}{SVM} & Arousal & **63.06** & **60.73** \\ \cline{2-5}  & \multirow{2}{*}{SVM} & Valence & **62.77** & **61.46** \\ \cline{2-5}  & \multirow{2}{*}{Arusal} & Arousal & **62.85** & **61.06** \\ \hline \end{tabular}
\end{table} 
Table 6: Machine learning performance after applying the filter-based feature selection method. Improved results compared to models without feature selection are marked in boldface. Logistic regression demonstrated a noticeable improvement, achieving an accuracy of 64.30% for valence and 64.35% for arousal, compared to the baseline accuracy of 60.90% and 63.18%, respectively. This corresponds to a performance increase of +3.40% for valence and +1.17% for arousal. Similarly, the SVM model exhibited enhancements in both accuracy and F1 scores for both labels after feature selection. Specifically, the accuracy improved to 63.88% (+2.29%) for valence and 63.89% (+0.83%) for arousal. XGBoost showed a marginal performance increase post-feature selection, with valence accuracy rising to 63.77% (+0.71%) and arousal accuracy to 62.08% (+1.40%). This indicates a modest benefit from the feature reduction process. For the RF, the results also reflected an enhancement. The accuracy for valence increased to 66.45% (+0.32%) and for arousal to 61.52% (+1.18%), suggesting that reducing the feature set did not detract from its performance.