## 2 Related Works
### Previous Work
#### 2.4.2 The Previous Method
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**FM**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Metric**} & \multicolumn{2}{c}{**Previous Work**} & \multicolumn{2}{c}{**Transfer**} & \multicolumn{2}{c}{**Parallelize**} \\  & & & **TT-VEDL-ST** & & **Data-Driven Features** & \multicolumn{2}{c}{**Training Loop**} & \\ \cline{3-10}  & & & **TT-VEDL-ST** & & **Option** & **Option** & [8; 9; 10; 11] & **CMP** & [12; 13; 14; 15] & [34] & \\ \hline \multirow{4}{*}{HLMAN-} & Accuracy & **100.00** & **98.80** & **98.80** & 85.54 & 79.52 & 95.18 & 95.18 & 96.39 \\ \cline{2-10}  & (\%) & F1-Score & **1.00** & **0.99** & **0.99** & 0.86 & 0.79 & 0.95 & 0.95 & 0.96 \\ \cline{2-10}  & HUMAN\_ACTS & Precision & **1.00** & **0.99** & **0.99** & 0.86 & 0.80 & 0.95 & 0.95 & 0.96 \\ \cline{2-10}  & Recall & **1.00** & **0.99** & **0.99** & 0.86 & 0.77 & 0.95 & 0.95 & 0.96 \\ \cline{2-10}  & Sensitivity & **1.00** & **0.99** & **0.99** & 0.86 & 0.77 & 0.95 & 0.95 & 0.96 \\ \cline{2-10}  & Specificity & **1.00** & **1.00** & **1.00** & 0.95 & 0.94 & 0.98 & 0.98 & 0.99 \\ \cline{2-10}  & Accuracy & **100.00** & 95.76 & 94.92 & 85.59 & 86.44 & 95.76 & **96.61** & 96.61 \\ \cline{2-10}  & \multirow{4}{*}{LOOP\_SENSOR} & F1-Score & **1.00** & 0.96 & 0.94 & 0.85 & 0.87 & 0.96 & **0.97** & 0.97 \\ \cline{2-10}  & Precision & **1.00** & 0.96 & 0.95 & 0.86 & 0.87 & 0.96 & **0.97** & 0.97 \\ \cline{2-10}  & Recall & **1.00** & 0.96 & 0.94 & 0.85 & 0.86 & 0.96 & **0.97** & 0.97 \\ \cline{2-10}  & Sensitivity & **1.00** & 0.96 & 0.94 & 0.85 & 0.86 & 0.96 & **0.97** & 0.97 \\ \cline{2-10}  & Specificity & **1.00** & 0.99 & 0.99 & 0.98 & 0.98 & 0.99 & **0.99** & 1.00 \\ \hline \multirow{4}{*}{MLFM} & Accuracy & **100.00** & **97.59** & **98.80** & 72.29 & 75.90 & 95.18 & **97.59** & 96.39 \\ \cline{2-10}  & (\%) & F1-Score & **1.00** & **0.98** & **0.99** & 0.55 & 0.77 & 0.95 & **0.98** & 0.96 \\ \cline{2-10}  & HUMAN\_ACTS & Precision & **1.00** & **0.99** & **0.99** & 0.80 & 0.78 & 0.95 & **0.98** & 0.96 \\ \cline{2-10}  & Recall & **1.00** & **0.98** & **0.99** & 0.42 & 0.75 & 0.95 & **0.98** & 0.96 \\ \cline{2-10}  & Sensitivity & **1.00** & **0.98** & **0.99** & 0.42 & 0.75 & 0.95 & **0.98** & 0.96 \\ \cline{2-10}  & Specificity & **1.00** & **1.00** & **1.00** & 0.96 & 0.93 & 0.98 & **0.99** & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Evaluation of best performance for each dataset and target task model in classification using representative metrics. Overall performances for regression are best in order of TT-VFDL-ST, previous work [7], ref. [34], BASE, refs. [12; 13; 14; 15], refs. [8; 9; 10; 11], and CMP. That is, TT-VFDL-ST showed the best performance among all methods with all setups. Also, our previous work [7] of option DD and option RD shows the second and third high performance, respectively. Among the other methods, ref. [34] shows higher performance than BASE in most setups and also fourth in performance. In some setups, refs. [12; 13; 14; 15], refs. [8; 9; 10; 11], and CMP show higher performance than BASE, but in most setups, they show lower performance than BASE. Likewise, overall performances for classification are best in order of TT-VFDL-ST, previous work [7], ref. [34], BASE, refs. [12; 13; 14; 15], refs. [8; 9; 10; 11], and CMP. Also, TT-VFDL-ST showed the best performance among all methods with all setups even in classification. Our previous work [7] shows the second high performance. Unlike regression, the two options of our previous work [7] show similar performance to each other in classification. Among the other methods, ref. [34] shows the highest performance but slightly higher performance than BASE. Refs. [12; 13; 14; 15] shows slightly lower performance than BASE. Especially in classification, [8; 9; 10; 11] and CMP show lower performance than BASE in every setup. In Table 9, performances for each classification dataset and each method are summarized in detail using the true-positive (TP), false-positive (FP), true-negative (TN), and false-negative (FN) values.