## 4 Experiment Verification
### Experimental Results
#### 4.2.1 Comparison without Transfer with Individual Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test** & **Model** & \begin{tabular}{c} **Accuracy of** \\ **Training/Loss** \\ \end{tabular} & \
\begin{tabular}{c} **Accuracy of** \\ **Validation/Loss** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy of** \\ **Testing/Loss** \\ \end{tabular} & **Time of Training** \\ \hline Test1 & DTLCL & (0.884 \(\pm\) 0.52)/0.42 & (0.876 \(\pm\) 0.43)/0.43 & (0.885 \(\pm\) 0.55)/0.43 & 18 s \\  & CNN & (0.786 \(\pm\) 0.63)/0.64 & (0.731 \(\pm\) 0.72)/1.66 & (0.723 \(\pm\) 0.64)/1.78 & 22 s \\  & LSTM & (0767 \(\pm\) 0.51)/1.52 & (0.728 \(\pm\) 0.62)/1.54 & (0.707 \(\pm\) 0.62)/1.53 & 26 s \\  & DNN & (0.602 \(\pm\) 3.52)/1.57 & (0.634 \(\pm\) 3.52)/1.64 & (0.645 \(\pm\) 3.54)/1.47 & 35 s \\  & AE & (0.540 \(\pm\) 3.52)/0.75 & (0.562 \(\pm\) 3.87)/1.44 & (0.524 \(\pm\) 3.72)/1.23 & 24 s \\  & KNN & (0.552 \(\pm\) 1.22) & (0.541 \(\pm\) 1.29) & (0.506 \(\pm\) 1.32) & 107 s \\  & SVM & (0.679 \(\pm\) 3.82) & (0.651 \(\pm\) 3.38) & (0.661 \(\pm\) 3.38) & 19 s \\  & DTLCL\_A & (0.802 \(\pm\) 0.41)/0.61 & (0.798 \(\pm\) 0.51)/0.58 & (0.805 \(\pm\) 0.45)/0.54 & 17 s \\  & DTLCL\_B & (0.791 \(\pm\) 0.58)/0.52 & (0.787 \(\pm\) 0.64)/0.53 & (0.784 \(\pm\) 0.55)/0.57 & 16 s \\  & MMBT\_mmbt & (0.754 \(\pm\) 0.51)/1.43 & (0.713 \(\pm\) 0.63)/1.48 & (0.718 \(\pm\) 0.60)/1.52 & 25 s \\ \hline Test2 & DTLCL & (0.953 \(\pm\) 0.32)/0.38 & (0.946 \(\pm\) 0.33)/0.36 & (0.945 \(\pm\) 0.34)/0.37 & 19 s \\  & CNN & (0.886 \(\pm\) 0.43)/0.58 & (0.872 \(\pm\) 0.41)/1.32 & (0.877 \(\pm\) 0.43)/1.22 & 23 s \\  & LSTM & (0854 \(\pm\) 0.51)/0.62 & (0.824 \(\pm\) 0.52)/1.48 & (0.835 \(\pm\) 0.54)/1.37 & 30 s \\  & DNN & (0.872 \(\pm\) 2.50)/1.23 & (0.874 \(\pm\) 2.54)/1.35 & (0.885 \(\pm\) 2.51)/1.33 & 39 s \\  & AE & (0.630 \(\pm\) 3.42)/0.52 & (0.664 \(\pm\) 3.37)/1.12 & (0.654 \(\pm\) 3.22)/1.01 & 27 s \\  & KNN & (0.652 \(\pm\) 1.01) & (0.641 \(\pm\) 1.12) & (0.616 \(\pm\) 1.28) & 118 s \\  & SVM & (0.859 \(\pm\) 3.82) & (0.840 \(\pm\) 3.38) & (0.841 \(\pm\) 3.38) & 22 s \\  & DTLCL\_A & (0.892 \(\pm\) 0.41)/0.52 & (0.898 \(\pm\) 0.51)/0.53 & (0.895 \(\pm\) 0.45)/0.51 & 18 s \\  & DTLCL\_B & (0.863 \(\pm\) 0.58)/0.54 & (0.868 \(\pm\) 0.64)/0.56 & (0.887 \(\pm\) 0.55)/0.53 & 17 s \\  & MMBT\_mmbt & (0.845 \(\pm\) 0.54)/0.57 & (0.831 \(\pm\) 0.52)/1.38 & (0.851 \(\pm\) 0.53)/1.36 & 25 s \\ \hline \hline \end{tabular} Note: The results are formatted as average (accuracy \(\pm\) standard deviation)/error rate.  
\end{table} 
Table 5: Average results over ten trials for six compared models. and is determined by the number of samples. The accuracy of the test dataset increases with sample size. More focused studies have been conducted to further confirm the efficacy of the proposed DTLCL. In this section, the P, R and F values of several models are presented. Figure 9 shows the P-values of DTLCL and the other five models in the test data set for experiments. The P accuracy of DTLCL in Test 1 and Test 2 is the highest, especially in N, IF, RF, OF. The corresponding P-values of the other models are less than 60% in Test 1 and 70% in Test 2. Less than 50% in Test 1 and 65% in Test 2 show that the P-rate of KNN is extremely low at six errors. On the other hand, the precision rate is consistent at six errors  
Figure 8: Comparison of accuracy of different methods in five experiments. and the value of the precision rate of the DTLCL model has increased to more than 80% in Test 1 and 95% in Test 2. Figure 10 shows the recognition rate of DTLCL and six other models in the test data set. The results show that DTLCL has a higher recognition rate than the other models, especially for N, RF, GPF in Test 1 and N, IF, RF, OF, GPF in Test 2. The average recognition rate of the other models is less than 45% in Test 1 and 90% in Test 2. In contrast, the average recognition rate of the DTLCL models increases to more than 75% in Test 1 and 92% in Test 2. Figure 9: P of different models. Precision and recall are well developed in DTLCL, but they cannot comprehensively and objectively evaluate the results of the models.