## 3 Case Study: Experimental Verification and Comparison Results
### Comparison Studies
#### 3.2.3 Comparison of Model Performance According to Domain Optimization Methods  
The most critical part of instance-based transfer learning is the domain optimization process using instance weighting to minimize dissimilarity between domain instances. To demonstrate the effectiveness of the multi-objective instance weight of the proposed method, we compared the results according to the five domain optimization methods by performing transfer learning in the same process. In addition, each optimization method has different characteristics and merits, and the performance is greatly affected by the number of target domain data. Therefore, we compared the results through some scenarios in which a number from 20 to 180 of the target training data were randomly removed. The test set used to calculate the accuracy was fixed at the initial 180 to remove the randomness and observe only the effect of the number of target training data. In Table 5, in all cases, the diagnosis accuracy of instance-based transfer learning with domain optimization was higher than that of the transfer learning without the domain optimization process. If domain optimization is not performed, it was confirmed that negative transfer occurs in some scenarios. The proposed method had the highest diagnosis accuracy in all cases. The other three optimization methods have similar results over the entire range but have different pros and cons. These results are presented in Table 6. The highest diagnosis accuracy was obtained using the proposed method like other accuracy comparisons. The proposed method showed 97.5%, 93.85%, 81.49%, and 91.5% accuracies for S1, S2, S3, and S4, respectively. The target dropout-based method demonstrated a low variance in the average accuracy with 94.95%, 89.39%, 78.84%, and 88.25% accuracy for S1, S2, S3, and S4, respectively. The KLD weight-based method has 95.32%, 89.02%, 75.29%, and 87.2% accuracy, respectively. The MMD weight-based method has 95.76%, 90.85%, 79.96%, and 89.38% accuracy, respectively, showing higher diagnostic accuracy than the other two domain optimization methods. A detailed comparison of accuracy over the entire range is presented in Figure 7. The results confirmed that excellent domain optimization effect can be obtained by complementarily using the advantages of the two indicators for weighting. Figure 6: Accuracy difference between the proposed and non-transfer learning methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Transfer** \\ **Scenario** \\ **(Number of Training Data)** \\ \end{tabular} & \begin{tabular}{c} **No** \\ **Optimization** \\ \end{tabular} & \begin{tabular}{c} **Target** \\ **Dropout** \\ \end{tabular} & \
\begin{tabular}{c} **KLD Instance** \\ **Weights** \\ \end{tabular} &
\begin{tabular}{c} **MMD Instance** \\ **Weights** \\ **(Ours)** \\ \end{tabular} \\ \hline S1 (from 130 to 180) & 92.96 & 94.95 & 95.32 & 95.76 & 97.5 \\ S2 (from 70 to 120) & 89.3 & 89.39 & 89.02 & 90.85 & 93.85 \\ S3 (from 20 to 60) & 74.87 & 78.84 & 75.29 & 79.96 & 81.49 \\ S4 (from 20 to 180) & 86.35 & 88.25 & 87.2 & 89.38 & 91.5 \\ \hline \end{tabular}
\end{table} 
Table 6: Average accuracy according to the transfer scenario.