# Few-Shot Wideband Tympanometry Classification in
## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source Datasets** & **Approaches** & **F1-Score** & **AUC Values** \\ \hline \multirow{3}{*}{_SynDatal_} & FT & \(94.36\pm 1.13\) & \(96.54\pm 1.39\) \\  & DAN & \(94.60\pm 1.07\) & \(96.22\pm 0.80\) \\  & GPGDA & \(\mathbf{95.71\pm 0.99}\) & \(\mathbf{97.61\pm 1.13}\) \\ \hline \multirow{3}{*}{_SynDatalN_} & FT & \(94.69\pm 1.02\) & \(96.84\pm 1.05\) \\  & DAN & \(94.76\pm 0.67\) & \(97.01\pm 1.33\) \\  & GPGDA & \(\mathbf{95.97\pm 1.06}\) & \(\mathbf{97.75\pm 1.21}\) \\ \hline \multirow{3}{*}{_SynDatalM_} & FT & \(93.85\pm 1.59\) & \(96.90\pm 0.98\) \\  & DAN & \(\mathbf{95.83\pm 1.41}\) & \(97.34\pm 1.26\) \\ \cline{1-1}  & GPGDA & \(95.74\pm 0.94\) & \(\mathbf{97.88\pm 1.10}\) \\ \hline \hline \end{tabular} Values are expressed as mean \(\pm\) standard deviation (\(r=10\) experimental replicates) of percentages. \(\lambda=0.5\) in GPGDA. \(p<0.01\) for the effect of approach types and not significant for the effect of source datasets for both AUC and F1-score; two-way ANVOA. For AUC, \(p<0.01\) for GPGDA versus FT and \(p<0.05\) for GPGDA versus DAN; for F1-score, \(p<0.001\) for GPGDA versus FT and \(p<0.05\) for GPGDA versus DAN; [PERSON]’s multiple comparison test, adjusted \(p\)-values for multiple comparisons.  
\end{table} 
Table 1: Performance of the GPGDA algorithm in comparison with the baseline methods on the WBT dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hyperparameter** & **AUC/_SynDatal_** & **AUC/_SynDatalN_** & **AUC/_SynDatalM_** \\ \hline \(\lambda=0\) & \(96.70\pm 1.15\) & \(96.94\pm 1.10\) & \(96.83\pm 0.68\) \\ \(\lambda=0.5\) & \(\mathbf{97.61\pm 1.13}\) & \(97.75\pm 1.21\) & \(\mathbf{97.88\pm 1.10}\) \\ \(\lambda=1\) & \(97.58\pm 1.36\) & \(\mathbf{98.02\pm 1.44}\) & \(97.11\pm 1.18\) \\ \(\lambda=2\) & \(96.50\pm 1.59\) & \(97.26\pm 1.47\) & \(97.39\pm 1.26\) \\ \hline \hline \end{tabular} Values are expressed as mean \(\pm\) standard deviation (\(r=10\) experimental replicates) of percentages. \(p<0.05\) for the effect of different \(\lambda\) values and not significant for the effect of source datasets on AUC; two-way ANVOA. \(p<0.05\) for \(\lambda=0.5\) versus \(\lambda=0\) and not significant for the other cases; [PERSON]’s multiple comparison test, adjusted \(p\)-values for multiple comparisons.  
\end{table} 
Table 2: AUC performance of the GPGDA algorithm with different values of hyperparameter \(\lambda\) from 0 to 2.  
strategy in [5]). The classifiers take the parameters from the pre-training only if \(l^{CP}\) is enabled. Disabling \(l^{CP}\) resulted in a more severe performance degradation than disabling \(l^{CE}_{r}\). Additionally, disabling the two losses yielded a similar performance on the final classification to that of employing the pre-trained network directly. It did not explicitly utilize the realistic dataset and could be treated as the lower bound of the algorithm performance. Furthermore, the decay modes of the training losses when increasing the number of epochs were investigated. Two components \(l^{c}\) and \(l^{m}\) in \(l^{GP}\) were considered separately. The analysis for three losses involved in the GPGDA algorithm showed that \(l^{CE}_{r}\) and \(l^{c}\) followed the same steep one-phase exponential decay while \(l^{m}\) followed a different and more gradual decline (Figure 4). Figure 4: Training losses in the GPGDA algorithm versus Epochs with _SynDatal_. Average data from five folds could be fitted with a one-phase exponential decay equation (\(y=(y_{0}-p)\cdot e^{-kx}+p\), the bottom-right panel, \(R^{2}\): 0.68, 0.71, and 0.70 for \(l^{CE}_{r}\), \(l^{c}\), and \(l^{m}\) respectively). \(l^{CE}_{r}\) and \(l^{c}\) followed a similar decay (half-lives: 1.84 and 1.68, respectively, \(p>0.05\), extra-sum-of squares F-test), while \(l^{m}\) followed a more gradual decay (half-life: 4.46, \(p<0.001\), F-test). The same results were obtained with _SynDatalN_ and _SynDatalM_ (data not shown).