## 4 Tt-Vfd-Sim
### TT-VFDL-SIM for Classification
And the detailed options of TT-VFDL-SIM applied to each experimental unit were trained by changing the learning rate values of merging task training. Meanwhile, as summarized in Section 5.1.1, since all training datasets of the classification datasets were composed of balanced datasets, accuracy and F1-Score were adopted as representative performance metrics among several performance metrics for the classification target task. In addition, MSE and \(R^{2}\) were adopted as representative performance metrics for the regression target task. The subsequent experimental results are organized in separate tables for the regression target task and classification target task. #### 5.1.6 Summary of Experimental Results

The main experimental results of the subsequent experiments are briefly summarized as follows. Through this, the effectiveness of the task-driven transfer approach in the IoT domain was experimentally verified:

1. In Section 5.3, for both the regression target task and the classification target task, the average performance was good in the following order: proposed TT-VFDL-SIM, P-VFDL-SLG, CNTR, P-VFDL-SL, and DT-VFDL-AE. 2. Task-driven features are more effective in improving performance than data-driven features. 3. Task-driven features that also consider univariate partial tasks, such as P-VFDL-SLG, are more effective in improving performance than task-driven features that are optimized only for multivariate target tasks, such as P-VFDL-SL or CNTR. 4. Ultimately, fine-tuning the multivariate target task based on task-driven features optimized only for each univariate partial task, such as TT-VFDL-SIM, is most effective in improving performance. 5. In Section 5.3, method option DD had higher average performance than method option RD in TT-VFDL-SIM for the regression target task. In TT-VFDL-SIM for the classification target task, method option RR and method option DR showed similar performance. 6. Introducing similar feature spaces through a task-driven transfer approach is effective in improving performance. 7. Compared to P-VFDL-SL of the training loop parallelization approach, TT-VFDL-SIM reduced the communication amount required to achieve optimal performance by up to 721.87 times and at least 33.79 times. 8. In the detailed experiment results in Section 5.4, feature option DD(R) or DD(D)-Test(R) was effective in improving performance for the regression target task. For the classification target task, feature option DR(R) or DR(D)-Test(R) was effective in improving performance. 9. In the task-driven transfer approach, not only transfer learning for the target task but also transfer learning between similar feature spaces for partial tasks is effective in improving performance. 10. The performance of TT-VFDL-SIM was the highest when introducing similar feature spaces generated from decoded data with reconstruction performance with an MSE of up to 0.00007 or less and \(R^{2}\) of at least 0.99. ### LSTM-AE-Based Data Augmentation for the TT-VFDL-SIM

One of the main strategies of the task-driven transfer approach for the design of TT-VFDL-SIM is to introduce similar feature spaces. We generated decoded data by applying the LSTM-AE-based Data Augmentation technique in Figure 5 to each IoT time-seriesstream that consists of each dataset in the preparation stage of TT-VFDL-SIM, and the results are summarized in Table 7. Average MSE and average \(R^{2}\) in Table 7 are the averages of MSE and \(R^{2}\) for the decoded data generated from streams that consist of each dataset, and they refer to the reconstruction performance of the decoded data from each raw dataset. In order to generate similar feature spaces, the reconstruction performance of decoded data generated from raw data needs to be high. To achieve this, as summarized in Figure 5, the original data patterns of each dataset are split into small-sized sub-patterns by tumbling windowing.