## 4 Results
### Results on Class-Imbalanced Datasets
Furthermore, when we compare the performance of SigAugment on the 2016A-50 dataset to the baseline model on the 2016A dataset, we find that even though the number of samples in the training set is reduced by a factor of 5 while the degree of sample imbalance increases by a factor of 5, on the same \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**2016A-10**} & \multicolumn{3}{c}{**2016A-20**} & \multicolumn{3}{c}{**2016A-50**} \\ \cline{2-11}
**Methods** & **DAE** & \begin{tabular}{c} **SE-** \\ **MSFN** \\ \end{tabular} & **LSTM2** & **DAE** & \
\begin{tabular}{c} **SE-** \\ **MSFN** \\ \end{tabular} & **LSTM2** & **DAE** &
\begin{tabular}{c} **SE-** \\ **MSFN** \\ \end{tabular} & **LSTM2** \\ \hline Baseline & 66.05 & 71.08 & 70.06 & 56.58 & 63.64 & 48.00 & 59.35 & 54.05 & 42.59 \\ \hline Focal loss & 65.84 & 73.55 & 70.25 & 61.20 & 64.68 & 46.42 & 56.97 & 52.42 & 43.25 \\ Polynom\_fit\_SMOTE\_poly & 66.95 & 74.04 & 35.26 & 60.43 & 65.96 & 62.12 & 37.14 & 58.27 & 60.22 \\ ProWSyn & 67.28 & 73.46 & 18.19 & 52.95 & 66.26 & 32.84 & 34.82 & 58.09 & 59.87 \\ SMOTE\_IPF & 67.45 & 73.12 & 36.22 & 53.04 & 61.85 & 62.80 & 29.34 & 20.99 & 60.39 \\ Lee & 69.47 & 74.05 & 52.86 & 54.78 & 64.96 & 48.74 & 33.45 & 57.59 & 60.07 \\ \hline Jit & 67.81 & 70.89 & 71.67 & 62.97 & 62.93 & 58.27 & 57.88 & 54.80 & 34.49 \\ Rot & **72.94** & 77.78 & 81.15 & 63.29 & 71.83 & 72.58 & **60.87** & 60.78 & 67.56 \\ Flip & 69.25 & 74.36 & 73.24 & **63.61** & 70.87 & 71.35 & 50.87 & 60.52 & 67.13 \\ CS & 71.62 & 76.82 & 73.15 & 62.11 & 68.76 & 68.05 & 46.10 & 56.88 & 45.16 \\ Inv & 67.29 & 74.07 & 70.84 & 62.79 & 68.39 & 65.10 & 60.66 & 57.84 & 45.22 \\ SP & 69.21 & 77.57 & 76.52 & 63.26 & 73.40 & 74.27 & 60.66 & 64.18 & 64.14 \\ Sca & 66.05 & 71.04 & 68.48 & 51.12 & 62.69 & 51.97 & 49.72 & 54.10 & 43.51 \\ FCS & 70.39 & 75.41 & 76.69 & 63.24 & 70.74 & 73.36 & 50.21 & 62.65 & 69.98 \\ \hline SigAugment-C & 66.99 & 80.36 & **83.52** & 63.25 & 76.97 & 79.34 & 59.77 & 62.38 & 72.47 \\ SigAugment-R & 72.51 & **81.36** & 83.07 & 63.22 & **78.32** & **79.57** & 60.16 & **70.93** & **72.61** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: On three class-imbalanced datasets, 2016A-10, 2016A-20, and 2016A-50, the proposed methods were compared to existing representative class rebalancing methods and individual transformations. The accuracy (%) is calculated as the mean of three experiments. The best-performing individual model results are shown in bold, while the best-performing dataset results are shown in bold and red. test set, our data augmentation strategies achieve recognition accuracies comparable to the baseline model (60.16% vs. 66.05%, 70.93% vs. 71.08%, and 72.61% vs. 70.06%). Figure 7 shows the loss and accuracy of LSTM2 training on the 2016A-50 dataset using the proposed SigAugment-R method. By comparing with Figure 5b, it is clear that the gap between the training loss and validation loss of LSTM2 is quite small by using SigAugment-R. This shows that SigAugment-R is effective in preventing overfitting. In addition, we report the results of our experiments on 2016A-10a and 2016A-10b in the Appendix, and the results are shown in Table A1. We can draw similar conclusions. Comparing the results of the model on 2016A-10b and 2016A-50, we find that the sample size of the head category has a greater impact on the resampling approaches, as it can lead to more severe overfitting problems in the model. In contrast, it has less impact on the individual data augmentation transformations and almost no impact on our proposed automated data augmentation method. The individual data augmentation methods are scored according to the following rules: we rank all transformations from highest to lowest gain, with the top three receiving one point and the remaining receiving none. The total score for three models and three datasets is nine. Figure 8 shows the scoring results.