# An Improved Random Forest Approach on GAN-Based Dataset Augmentation for Fog Observation
## 3 Result
### Estimation of Fog Density  
In this study, the dataset was divided into training, validation, and test sets in a 6:2:2 ratio. For the augmented dataset, the test set data were randomly selected from the initial dataset, while the training and validation set data were randomly selected from the augmented data. (1) Estimation Accuracy  
Table 4 presents the performance results of different models trained on the dataset without data augmentation. The experimental results indicate that the ResNet50 model achieved the highest average accuracy when data augmentation was not applied. However, the improved Random Forest model, incorporating hybrid clustering selection, performed comparably to ResNet50 in terms of average accuracy, achieving the highest accuracy under light fog, fog, and dense fog conditions. Table 5 presents the performance of different models on the augmented dataset. With data augmentation, the estimation accuracy of all models improved significantly. Notably, the improved Random Forest model achieved further accuracy enhancements across all fog grades, reaching 89.8% in very dense fog and 91.7% in dense fog, with an average accuracy increase of 93.0%. Although the estimation accuracy of other models also improved after data augmentation, their average accuracy remained lower than that of the improved Random Forest model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Fog Density**} \\ \hline  & **Very Thick Fog** & **Thick Fog** & **Dense Fog** & **Moderate Fog** & **Light Fog** & **Total** \\ \hline VGG-16 (\%) & 63.2 & 73.3 & 84.1 & 85.5 & 87.7 & 83.9 \\ VGG-19 (\%) & 64.7 & 71.4 & 84.7 & 86.6 & 86.3 & 85.6 \\ ResNet-50 (\%) & 68.5 & 76.5 & 85.3 & 88.4 & 90.1 & 86.9 \\ DenseNet-169 (\%) & 64.7 & 69.3 & 83.4 & 89.6 & 90.1 & 85.8 \\ Random Forest (\%) & 55.1 & 68.7 & 83.4 & 88.9 & 90.8 & 84.1 \\ Random Forest based on hybrid clustering (\%) & 58.5 & 70.5 & 85.5 & 89.7 & 91.1 & 86.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fog density accuracy of different models trained by original data. Figure 5: The correlation coefficient map between features via PCC. Additionally, the improved Random Forest model increased its average accuracy by 2.9% compared to the original Random Forest model, rising from 90.1% to 93.0%. This improvement is largely attributed to the effectiveness of the hybrid clustering method. By combining hierarchical clustering with k-Medoids clustering techniques, this method not only optimized the decision tree selection process but also effectively avoided the common local optimum problem encountered in traditional hierarchical clustering. Moreover, since the k-Medoids process was repeated only twice, the increase in computation time was negligible, meaning that this method significantly improved accuracy without a substantial increase in computational cost. (2) Convergence Index  
The variation in the loss function for different methods is shown in Figure 6. The improved Random Forest model meets the convergence condition after 40 training epochs, while other models, such as VGG-16 and VGG-19, require 60 to 80 epochs to reach convergence. This demonstrates that the improved Random Forest model not only outperforms other models in classification performance but also offers significant advantages in training efficiency. The model's ability to converge quickly significantly reduces training time and computational cost. (3) Data Requirements and Model Generalization Ability  
Compared to existing deep learning methods in the literature (see Table 6), the improved Random Forest model performs exceptionally well on small datasets. [PERSON] et al. used a dataset containing 4841 images, achieving an accuracy of 88%; [PERSON] et al. employed a private dataset with 6048 images, reaching a maximum accuracy of 92.72%; [PERSON] et al. used the VID I dataset (containing 3033 images), achieving an accuracy of 98%; [PERSON] et al. and [PERSON] et al. used datasets with 5104 and 24,031 images, respectively, with accuracies of 72% and 87%.