## 5 Experimental Design
### Parameter Optimization
#### 5.4.2 Results of Fashion-MINST Dataset  
The same methodology was applied to the Fashion-MNIST dataset. Based on the ANOVA results, the optimal configuration for the 25:1 imbalanced dataset was determined to be (0.031, 0.05, 0.05, 3 \(\times\) 3). The results obtained from parameter optimization, specifically, the results of Model 5, were compared with the performance of the five models defined above. Figure 13 and Table 11 present the final results of the five models. The experimental findings demonstrate that adjusting the number of convolution kernels, their size, and the learning rate affects the performance of CNN models, especially in handling imbalanced datasets. Model 1 showed poor performance on minority class samples with a recall of 0.28  
Figure 12: Evaluation metrics of Model 5 and undersampling compared to Model 5 on various classes in the CIFAR-10 dataset.  
and an F1 score of 0.42. Model 2 adjusted the number of convolution kernels and improved the recall to 0.31, with a slight increase in overall accuracy. Model 3 further changed the kernel size to 5 \(\times\) 5, maintaining an accuracy of 0.84 but experiencing slight decreases in the recall and F1 score. Model 4, adjusting the learning rate, showed improved performance with a recall of 0.28 and an F1 score of 0.41. Model 5, which combined adjustments to kernel number, size, and learning rate, achieved the best performance with an overall accuracy of 0.87, recall of 0.38, and F1 score of 0.50. These results are consistent with those of the experiments on the CIFAR-10 dataset, highlighting the significant impact of parameter selection on the recognition performance of minority class samples.  
Table 12 presents the performance metrics for each class for the Fashion-MNIST dataset for Model 5 and the undersampled model, with bold text representing the results of Model 5. Both T-shirt and shirt are minority samples. Model 5 performed exceptionally well for class 1, with a recall of 0.97, an F1 score of 0.78, a G-mean of 0.95, and a P-mean of 0.77. In contrast, for class 6, the undersampled model achieved a recall and F1 score of 0.46 and 0.53, respectively, while these metrics significantly improved to 0.88 and 0.61 through parameter adjustments in Model 5.  
Specifically, the data in the table show that, although class 6 performed poorly after undersampling, its recall and F1 score significantly improved through parameter adjustments in Model 5. The results indicate that appropriate adjustments to the number and size of convolution kernels and the learning rate can effectively enhance the recognition performance of minority samples (especially class 6), thereby improving the model's overall performance when handling imbalanced datasets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **F1** & **G-Mean** & **P-Mean** \\ \hline
1 & 0.84 & 0.28 & 0.42 & 0.52 & 0.90 \\
2 & 0.85 & 0.31 & 0.44 & 0.55 & 0.91 \\
3 & 0.84 & 0.24 & 0.38 & 0.48 & 0.90 \\
4 & 0.83 & 0.28 & 0.41 & 0.52 & 0.88 \\
5 & 0.87 & 0.38 & 0.50 & 0.59 & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performance of minority class samples on the Fashion-MNIST dataset across five models.  
Figure 13: Performance metrics box plot of the Fashion-MNIST dataset across five models with model highlights.(The red line represents the median).