## 5 Experiments and Evaluation
### Performance of WS-UDA in Single-Source/Single-Target Setting  
The performance of our proposed WS-UDA compared to benchmark approaches for the Amazon, FDU-MTL, and Spam data sets is illustrated in Figures 6-9, respectively.  
In the Amazon data set, we have four domains, resulting in twelve separate transfer tasks. While there are six and two tasks for the FDU-MTL and Spam data sets, respectively. Figures 6 and 7 illustrate the performance with the Amazon data set. As demonstrated, WS-UDA's weighted sequential framework outperforms the baseline MAN algorithm without sequential learning and all other comparable baselines in the four transfer tasks. Compared to alternative baselines, WS-UDA enhances the performance of various individual transfer tasks by up to 5% and the overall performance of all twelve transfer tasks by 2.34%.  
Similar improvements are shown with the WS-UDA approach using the FDU-MTL and Spam data sets, as shown, in Figures 8 and 9, respectively. WS-UDA boosts the performance of individual transfer tasks by up to 4.1% for the FDU-MTL dataset and 2.88% for the Spam dataset. The average performance across all transfer tasks is 2.85% for the FDU-MTL dataset and 1.7% for the Spam dataset.  
Figure 8: _Cont._  
Figure 7: Target domain accuracy in Amazon data set where the target domain is (**a**) DVD and (**b**) kitchen. The single-source/single-target setting.  
Based on the discussed findings regarding WS-UDA performance, it is important to note that different target domains exhibit varying rates of improvement. This variability is influenced by the similarity of these target domains to other source domains. To elucidate this relationship, we investigate the correlation between domain similarity and the performance of the WS-UDA algorithm. Figure 10 illustrates the average similarity between each domain and the other domains for the Amazon, FDU-MTL, and Spam datasets. For example, the correlation between the book domain and other domains in the Amazon dataset and the performance of the WS-UDA model is evident in Figure 10a. This observation aligns with the degree of improvement shown in Figure 6a, where the task DVD \(\rightarrow\) books achieves the highest accuracy. In contrast, the task kitchen \(\rightarrow\) books demonstrates limited performance gain, illustrating an inverse relationship between the performance of WS-UDA and the similarity between the source and target domains.  
The average performance improvement achieved by the WS-UDA architecture, along with the average similarity of the domain to other domains, is summarized in Table 3. The normalized average cosine similarity for the three datasets is shown in Figure 11, demonstrating that WS-UDA can enhance adaptation performance the further apart the two domains are from each other. Thus, WS-UDA addresses the issue with modern UDA approaches, which require similarity between the two domains to avoid instability.  
Figure 8: Target domain accuracy in the FDU-MTL data set. The single-source/single-target setting.  
Figure 9: Target domain accuracy in the Spam data set. The single-source/single-target setting.  
Figure 10: Average cosine similarity between different domains. Different target domains are represented on the \(x\)-axis. The \(y\)-axis displays the average cosine similarity between the labeled samples from various source domains and the unlabeled samples from the target domain.