# A Source Domain Extension Method for Inductive Transfer Learning Based on Flipping Output
## 4 Experimental
### Discussion  
Consider the experimental results described in Section 4.3. The target variable in the abalone dataset is a quantitative label because it is the age of the abalone, while the objective variable in the wine quality dataset is a qualitative label because it is based on sensory evaluation by a wine expert. Therefore, it is considered that the labels in the wine quality dataset had variation. First, we discuss the accuracy of the two bagging (learning using target domain and learning using source domain). In the bagging learned using the target domain, the accuracy is improved as the number of target domains increases. On the other hand, bagging learned using the source domain shows a stable accuracy. It is important to note that as \(\alpha\) increases, the accuracy of bagging with the source domain tends to decrease. When there are many target domains, learning with source domains shows a worse accuracy than with target domains. Next, we discuss the accuracy of TrBagg, a conventional transfer learning method, under the conditions of this experiment. In the case of \(N_{T}=40,~{}60,~{}80\), it can be \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(N_{T}\) & \(\alpha\) & Bagging Using \(D_{T}\) & Bagging Using \(D_{S}\) & TrBagg & Proposed Method \\ \hline \multirow{3}{*}{40} & 0.2 & 58.02 & 62.71 & 64.41 & 66.84 \\  & 0.4 & 59.61 & 60.25 & 60.46 & 65.54 \\  & 0.6 & 53.31 & 59.46 & 60.03 & 64.43 \\ \hline \multirow{3}{*}{60} & 0.2 & 59.97 & 63.71 & 64.44 & 66.05 \\  & 0.4 & 60.98 & 62.90 & 63.74 & 67.01 \\  & 0.6 & 61.89 & 61.83 & 62.95 & 68.92 \\ \hline \multirow{3}{*}{80} & 0.2 & 63.55 & 62.71 & 66.21 & 70.34 \\  & 0.4 & 64.02 & 63.63 & 65.98 & 68.68 \\  & 0.6 & 63.51 & 62.44 & 62.64 & 70.35 \\ \hline \multirow{3}{*}{100} & 0.2 & 64.45 & 62.71 & 67.69 & 69.87 \\  & 0.4 & 64.79 & 62.99 & 67.21 & 69.89 \\ \cline{1-1}  & 0.6 & 64.52 & 61.98 & 61.29 & 68.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy of test data using the wine quality dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(N_{T}\) & \(\alpha\) & Bagging Using \(D_{T}\) & Bagging Using \(D_{S}\) & TrBagg & Proposed Method \\ \hline \multirow{3}{*}{40} & 0.2 & 68.18 & 69.17 & 76.73 & 82.92 \\  & 0.4 & 72.55 & 66.58 & 75.89 & 76.73 \\  & 0.6 & 69.31 & 65.50 & 71.47 & 76.81 \\ \hline \multirow{3}{*}{60} & 0.2 & 70.67 & 68.31 & 72.48 & 77.76 \\  & 0.4 & 70.01 & 66.96 & 73.83 & 74.74 \\  & 0.6 & 69.52 & 66.57 & 76.10 & 76.30 \\ \hline \multirow{3}{*}{80} & 0.2 & 71.62 & 67.42 & 76.98 & 77.01 \\  & 0.4 & 71.22 & 65.50 & 76.90 & 78.36 \\  & 0.6 & 70.85 & 65.26 & 73.95 & 75.14 \\ \hline \multirow{3}{*}{100} & 0.2 & 72.87 & 71.92 & 78.35 & 81.74 \\  & 0.4 & 72.99 & 66.14 & 74.88 & 80.91 \\ \cline{1-1}  & 0.6 & 72.52 & 65.78 & 71.53 & 78.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy of test data using the abalone dataset. seen that TrBagg exhibits higher accuracy than bagging learned using the target domain, under all conditions. However, when \(N_{T}=100\) and \(\alpha=0.6\), TrBagg exhibits lower accuracy than bagging. There are two possible causes. One is that when \(\alpha=0.6\), there were few classifiers that could be adopted from the source domain. The data used for learning relies on random sampling by bootstrap sampling, because it is not known which data in the source domain is in accordance with the target concept. In a source domain in which more than half the data is erroneously labeled, it is considered that many data items with erroneous labels will inevitably be extracted. Therefore, it can be inferred from the results above that many classifiers could not properly learn the target concept. The other possible cause was the evaluation method for classifier adoption. Each classifier learned from data in the source domain is adopted or discarded based on its accuracy for the target domain. In this process, the target-domain accuracy of bagging learned from the target domain is used as the adoption criterion.