## 4 Results and Analysis
### Result of DEAP Dataset
#### 4.3.3 Results of Wrapper-Based Feature Selection on the DEAP Dataset  
Table 8 illustrates the impact of applying wrapper-based feature selection using GA on the DEAP dataset, highlighting the performance metrics of various machine learning models with and without feature selection. The feature selection using a GA selected 76, 79, 64, 74, 70, 77, 54, and 59 features for different labels and machine learning algorithms. For the RF model, feature selection significantly improved. The accuracy increased from 60.34% to 62.15% (+1.81%) and the F1 score from 58.08 to 59.88 (+1.8%) for arousal classification. This indicates a clear enhancement in model performance with a reduced feature set.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. of Features** & **Model** & **Labels** & **Accuracy (\%)** & **F1 Score (\%)** \\ \hline \multirow{8}{*}{160} & \multirow{2}{*}{RF} & Valence & 66.13 & 65.77 \\ \cline{3-5}  & & Arousal & 60.34 & 58.08 \\ \cline{3-5}  & & Valence & 60.90 & 60.20 \\ \cline{3-5}  & \multirow{2}{*}{Regression} & Arousal & 63.18 & 57.90 \\ \cline{3-5}  & & Valence & 63.06 & 62.67 \\ \cline{3-5}  & & Arousal & 60.68 & 58.49 \\ \cline{3-5}  & & Valence & 61.59 & 60.67 \\ \cline{3-5}  & \multirow{2}{*}{SVM} & Arousal & 63.06 & 57.08 \\ \hline \multirow{8}{*}{34} & \multirow{2}{*}{RF} & Valence & **66.45** & **66.44** \\ \cline{3-5}  & & Arousal & **61.52** & **61.35** \\ \cline{3-5}  & \multirow{2}{*}{Logistic Regression} & Valence & **64.30** & **61.32** \\ \cline{3-5}  & & Arousal & **64.35** & **62.22** \\ \cline{3-5}  & & Valence & **63.77** & **63.48** \\ \cline{3-5}  & \multirow{2}{*}{SVM} & Arousal & **62.48** & **63.49** \\ \cline{3-5}  & & Valence & **63.88** & **62.41** \\ \cline{3-5}  & & Arousal & **63.89** & **62.54** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Machine learning performance after applying the embedded-based feature selection method. Improved results compared to models without feature selection are marked in boldface.  
Logistic regression also benefited from the feature selection, showing an improvement in accuracy from 60.90% to 63.52% (+2.62%) and the F1 score from 60.20% to 63.12% (+2.92%) for valence. The accuracy for arousal decreased from 63.18% to 62.84% (\(-\)0.34%), while the F1 score increased from 57.90% to 58.86% (+0.96%), demonstrating better performance with fewer features.  
XGBoost performance for valence improved from 63.06% to 64.65% (+1.59%) in accuracy and from 62.67% to 64.05% (+1.38%) in F1 score. The accuracy increased from 60.68% to 63.52% (+2.84%), with an F1 score improvement from 58.49% to 61.43% (+2.94%). These results highlight the GA's effectiveness in enhancing model accuracy and F1 scores by optimizing feature selection.  
For the SVM model for valence, the accuracy increased from 61.59% to 63.63% (+2.04%) and the F1 score from 60.67% to 63.34% (+2.67%). The classification for arousal, however, saw a slight decrease in accuracy from 63.06% to 62.84% (\(-\)0.22%) and an increase in the F1 score from 57.08% to 59.17% (+2.09%). This suggests that while SVM performance was slightly mixed, the overall trend was towards improved predictive capability with selected features.  
The results clearly demonstrate that the wrapper-based feature selection using a GA generally enhances model performance for both valence and arousal classifications across different machine learning models. This method provides a balanced improvement over the baseline and other feature selection techniques using a GA.