## 5 Conclusions  
The present study proposed a HEGS framework that resulted in increased performance for the classification of imbalanced data during an IMDb sentiment classification task. The classification performance of the proposed random-mixing EDA method was superior to those of the random replacement (original method), random insertion, random deletion, and random swapping methods. The BLEU-2 and self-BLEU-2 measurements also indicated that the GAN-based models generated more diverse training sentences. The proposed Shapley algorithm was applied during postprocessing to filter out generated sentences from the GAN-based models and achieve the augmentation of imbalanced data, resulting in a substantially increased classification performance of both the validation and test sets. The experimental results reveal that the application of data augmentation under the proposed HEGS framework increased classification performance, which was further increased by incorporating additional training sentences. In addition, the EDA method has a very fast \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Augmentation Model**} & \multicolumn{2}{c}{**Validation Set**} & \multicolumn{2}{c}{**Test Set**} \\ \cline{2-5}  & **Macro F1** & **SD of F1** & **Macro F1** & **SD of F1** \\ \hline Without & & & & \\ (only using imbalanced data) & 0.841 & 0.0134 & 0.832 & 0.0083 \\ EDA & 0.834 & 0.0114 & 0.828 & 0.0084 \\ CatGAN & 0.828 & 0.0130 & 0.826 & 0.0055 \\ SentiGAN & 0.826 & 0.0090 & 0.824 & 0.0090 \\ EDA + CatGAN & 0.848 & 0.0123 & 0.830 & 0.0071 \\ EDA + SentiGAN & 0.834 & 0.0114 & 0.832 & 0.0084 \\ EDA + CatGAN + Shapley & 0.860 & 0.0071 & 0.854 & 0.0090 \\ EDA + SentiGAN + Shapley & 0.860 & 0.0071 & 0.854 & 0.0055 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification performance based on Macro F1 and F1 standard deviation (SD) in five-fold cross validation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Validation Set**} & \multicolumn{2}{c}{**Test Set**} \\ \cline{2-5}  & **Macro F1** & **SD F1** & **Macro F1** & **SD F1** \\ \hline EDA(RM) & 0.834 & 0.0114 & 0.828 & 0.0084 \\ EDA(SR) & 0.830 & 0.0100 & 0.828 & 0.0110 \\ EDA(RI) & 0.834 & 0.0084 & 0.826 & 0.0055 \\ EDA(RS) & 0.830 & 0.0071 & 0.822 & 0.0110 \\ EDA(RD) & 0.832 & 0.0110 & 0.828 & 0.0084 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification performances of different EDA methods in five-fold cross validation. processing time to generate similar sentences, but GAN-based models and BERT models need more time consuming to obtain a best trained model. Therefore, computation ability is a big challenge for our proposed framework. Future research should be focused in three directions. First, the HEGS framework could be applied to augment dialogue data and address the lack of labeled dialogue data. Second, more powerful text generation methods (e.g., GPT-2 or XLNet models) could be applied to generate more diverse sentences and further reduce the problem of imbalanced data classification. Third, different imbalanced ratios could evaluate the model performances, what number of samples of minority classes could be learned and consequently, obtain better results with the HEGS framework which would mean a stable model. Conceptualization, J.-L.W. and S.H. ; methodology, J.-L.W. and S.H. ; software, J.-L.W. and S.H. ; validation, J.-L.W. and S.H. ; formal analysis, S.H. ; investigation, J.-L.W. ; resources, J.-L.W. and S.H. ; data curation, [PERSON]-L.W. and S.H. ; writing--original draft preparation, [PERSON]-L.W. and S.H. ; writing--review and editing, [PERSON]-L.W. ; visualization, S.H. ; supervision, [PERSON]-L.W. ; project administration, [PERSON]-L.W. ; funding acquisition, [PERSON]-L.W. All authors have read and agreed to the published version of the manuscript. This research was partially supported by the Ministry of Science and Technology, Taiwan (Grant numbers: MOST 110-2221-E-031-004, and MOST 111-2221-E-031-004-MY3). Not applicable.