# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
## 3 Results
### Model Classification Results and Performance Comparison  
#### 3.2.1 Binary Classification  
Villages 1 and 2 were utilized as the training models, while Village 3 served as the test model. The training and test sets were randomly selected, and SMOTE along with Bayesian optimization were applied to enhance the model's performance. In binary classification, the target variable values were \"\(-1\)\" and \"\(1\)\", with 36 signal features as independent variables, and valence and arousal as the dependent variables. The model performance results are presented in Table 4 and Figure 9.  
The binary classification results indicate that the model's recognition accuracy for emotional pleasure, based on XGBoost and RF, surpasses 80%. For emotional arousal, both the XGBoost- and RF-based models achieve recognition accuracy rates exceeding 75%, demonstrating robust classification performance. These findings further suggest that both models are effective in assessing the emotional quality of rural visual landscapes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Emotional** & **Classifier** & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline \multirow{4}{*}{Valence} & XGBoost & 82.1\% & 0.918 & 0.867 & 0.891 \\  & RF & 84.0\% & 0.941 & 0.870 & 0.904 \\  & DT & 67.9\% & 0.718 & 0.859 & 0.782 \\  & LR-GD & 69.8\% & 0.694 & 0.908 & 0.787 \\ \hline \multirow{4}{*}{Arousal} & XGBoost & 77.9\% & 0.847 & 0.859 & 0.853 \\  & RF & 75.8\% & 0.875 & 0.818 & 0.846 \\ \cline{1-1}  & DT & 69.5\% & 0.792 & 0.803 & 0.797 \\ \cline{1-1}  & LR-GD & 64.2\% & 0.653 & 0.839 & 0.734 \\ \hline \hline \end{tabular}  
* “\(\%\)” indicates accuracy.  
\end{table}
Table 4: Performance comparison of binary classification models based on different classifiers for valence and arousal.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Classifier**} & \multicolumn{5}{c}{**47 Features**} & \multicolumn{5}{c}{**36 Features**} \\ \cline{3-10}  & & **Accuracy** & **Recall** & **Precision** & **F1-Score** & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline \multirow{2}{*}{Binary} & XGBoost & 79.3\% & 0.918 & 0.839 & 0.876 & 82.1\% & 0.918 & 0.867 & 0.891 \\  & RF & 79.3\% & 0.918 & 0.839 & 0.876 & 84.0\% & 0.941 & 0.870 & 0.904 \\ \hline \multirow{2}{*}{Ternary} & XGBoost & 65.4\% & 0.654 & 0.654 & 0.630 & 77.2\% & 0.772 & 0.765 & 0.766 \\  & RF & 62.5\% & 0.625 & 0.606 & 0.611 & 64.0\% & 0.640 & 0.646 & 0.642 \\ \hline \hline \end{tabular}  
* “\(\%\)” indicates accuracy.  
\end{table}
Table 2: Comparison of model valence performance based on 47 features and 36 features.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Classifier**} & \multicolumn{5}{c}{**47 Features**} & \multicolumn{5}{c}{**36 Features**} \\ \cline{3-10}  & & & **Accuracy** & **Recall** & **Precision** & **F1-Score** & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline \multirow{2}{*}{Binary} & XGBoost & 72.9\% & 0.804 & 0.816 & 0.810 & 77.9\% & 0.847 & 0.859 & 0.853 \\  & RF & 70.8\% & 0.750 & 0.811 & 0.780 & 75.8\% & 0.875 & 0.818 & 0.846 \\ \hline \multirow{2}{*}{Ternary} & XGBoost & 59.6\% & 0.596 & 0.591 & 0.582 & 74.3\% & 0.743 & 0.740 & 0.738 \\  & RF & 57.4\% & 0.574 & 0.556 & 0.558 & 58.1\% & 0.581 & 0.578 & 0.579 \\ \hline \hline \end{tabular}  
* “\(\%\)” indicates accuracy.  
\end{table}
Table 3: Comparison of model arousal performance based on 47 features and 36 features.