# Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN +
## 4 Case Analysis
### Analysis of Network Data Augmentation and Pattern Recognition Effectiveness
#### 4.3.1 Applicability of DAE-GAN  
To evaluate the applicability of the data augmentation method proposed, a Support Vector Machine (SVM) [32] with a radial basis function kernel and a classifier network structure of 13-5000-400-1 sparse autoencoder (SAE) [33] were included for comparison. The classifiers were trained using both the original sample set and the sample set expanded with DAE-GAN generated samples. Following the training phase, the classifiers were utilized to conduct pattern recognition analysis on partial discharge signals, and the evaluation metrics are compared in Table 5.  
In Table 5, \(\lambda_{accuracy}\%\), \(\lambda_{F1}\%\), and \(\lambda_{G-mean}\%\) represent three common metrics used to evaluate machine learning models in classification tasks, each with a distinct focus, especially in the context of imbalanced datasets. \(\lambda_{accuracy}\%\) is a widely used metric for evaluating classification models, reflecting the ratio of correctly classified samples to the total sample size. It is particularly effective when the dataset has a relatively balanced distribution of positive and negative samples. \(\lambda_{F1}\%\) is a metric that combines Precision and Recall, calculated as the harmonic mean of these two metrics. It is particularly useful for evaluating model performance on imbalanced datasets, where the focus is on balancing Precision and Recall. \(\lambda_{G-mean}\%\), on the other hand, is specifically designed for imbalanced datasets, measuring the model's balance between positive and negative class performance. It is particularly effective in situations where the dataset is highly imbalanced. The selection of these three metrics for evaluating model performance is based on their distinct focuses, which are suitable for different data distributions and task requirements. In particular, they offer a comprehensive evaluation of model performance in classification tasks with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **CWGAN-GP** & **BEGAN** & **VAE** & **DAE-GAN** \\ \hline MS-SSIM values & 0.8463 & 0.7979 & 0.7838 & 0.8680 \\ \hline \hline \end{tabular}
\end{table}
Table 4: MS-SSIM index of probability distribution of different models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{2}{c}{\(\lambda_{accuracy}\%\)} & \multicolumn{2}{c}{\(\lambda_{F1}\%\)} & \multicolumn{2}{c}{\(\lambda_{G-mean}\%\)} \\ \cline{2-7}  & **Before** & **After** & **Before** & **After** & **Before** & **After** \\  & **Augmentation** & **Augmentation** & **Augmentation** & **Augmentation** & **Augmentation** & **Augmentation** \\ \hline SVM & 78.74 & 88.89 & 77.92 & 88.25 & 78.20 & 88.33 \\ SAE & 87.51 & 91.05 & 85.56 & 92.59 & 87.83 & 92.88 \\ Classifier used & 90.12 & 98.36 & 88.62 & 97.82 & 89.93 & 97.62 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of classification effect before and after data augmentation.  
imbalanced datasets, each providing a unique perspective. This helps to avoid the bias of relying on a single metric, thereby enhancing the credibility of the evaluation results.  
By comparing the evaluation metrics of each classifier before and after data augmentation in Table 5, it is evident that all classifiers show an improvement in their evaluation metrics after DAE-GAN data augmentation. This demonstrates that the proposed data augmentation method is effective and applicable across various classifiers, including SVM and SAE, as well as the specific classifier utilized in this study.