## 4 Results and Discussion
### Experiment III: The Availability of Transfer Learning Method Based on HAs
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Classifier** & \multicolumn{2}{c}{**XLNet + HA + FC + TL**} & \multicolumn{2}{c}{**XLNet + BiLSTM + HA + FC**} & \multicolumn{2}{c}{**XLNet + BiLSTM + HA + FC + TL**} \\ \cline{2-9}
**No.** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline C00000 & 0.970 & 0.967 & 0.969 & 0.971 & 0.966 & 0.968 & 0.971 & 0.966 & 0.968 \\ C01000 & 0.956 & 0.934 & 0.944 & 0.961 & 0.946 & 0.953 & 0.961 & 0.946 & 0.953 \\ C01100 & 0.984 & 0.993 & 0.989 & 0.988 & 0.995 & 0.992 & **0.995** & **0.995** & **0.995** \\ C01110 & 0.898 & 0.912 & 0.896 & **0.949*** & 0.890 & 0.915 & 0.937 & **0.925** & **0.928** \\ C01120 & 0.989 & 0.969 & 0.978 & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** \\ C01200 & 0.956 & 0.945 & 0.950 & **0.964** & 0.936 & 0.949 & 0.953 & **0.948** & **0.951** \\ C01300 & 0.929 & 0.923 & 0.926 & 0.882 & 0.882 & 0.882 & **0.938** & **0.929** & **0.933** \\ C01310 & 0.962 & 0.972 & 0.966 & **0.980** & **0.986** & **0.983** & **0.980** & **0.986** & **0.983** \\ C01320 & 0.967 & 0.948 & 0.954 & 0.950 & 0.926 & 0.924 & **0.968** & **0.931** & **0.943** \\ C01400 & 0.915 & 0.943 & 0.928 & 0.846 & 0.922 & 0.867 & **0.909** & **0.961** & **0.930** \\ C01410 & 0.841 & 0.785 & 0.804 & 0.740 & 0.797 & 0.752 & **0.764** & **0.789** & **0.769** \\ C01420 & 0.550 & 0.727 & 0.611 & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** \\ C01500 & 0.880 & 0.875 & 0.877 & 0.847 & 0.814 & 0.826 & **0.940** & **0.916** & **0.926** \\ C01510 & 1.000 & 1.000 & 1.000 & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** \\ C01600 & 0.978 & 0.941 & 0.958 & **0.978** & **0.941** & **0.958** & **0.978** & **0.941** & **0.958** \\ C02000 & 0.989 & 0.982 & 0.986 & 0.994 & 0.987 & 0.990 & 0.994 & 0.987 & 0.990 \\ C02100 & 0.996 & 0.992 & 0.994 & **1.000** & **0.998** & **0.999** & **1.000** & **0.998** & **0.999** \\ C02110 & 0.984 & 0.973 & 0.978 & **0.990** & **0.980** & **0.985** & 0.987 & 0.979 & 0.983 \\ C02120 & 0.992 & 0.992 & 0.992 & 0.990 & 0.995 & 0.992 & **1.000** & **1.000** & **1.000** \\ C02200 & 0.859 & 0.829 & 0.781 & **0.900** & **0.833** & **0.813** & 0.885 & 0.832 & 0.804 \\ C02300 & 1.000 & 1.000 & 1.000 & 0.979 & 0.997 & 0.988 & **1.000** & **1.000** & **1.000** \\ C02310 & 1.000 & 1.000 & 1.000 & 0.996 & 0.965 & 0.980 & **1.000** & **1.000** & **1.000** \\ C03000 & 0.919 & 0.914 & 0.916 & 0.924 & 0.932 & 0.928 & 0.924 & 0.932 & 0.928 \\ C04000 & 0.986 & 0.987 & 0.987 & 0.995 & 0.994 & 0.994 & 0.995 & 0.994 & 0.994 \\ C04100 & 1.000 & 1.000 & 1.000 & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** \\ C04200 & 0.995 & 0.992 & 0.993 & 0.987 & 0.987 & 0.987 & **1.000** & **1.000** & **1.000** \\ C04300 & 0.857 & 0.882 & 0.869 & **0.996** & 0.984 & 0.990 & 0.984 & **0.996** & **0.990** \\ Avg(C) & 0.939 & 0.940 & 0.935 & 0.956 & 0.950 & 0.949 & **0.965** & **0.961** & **0.960** \\ \hline \hline \end{tabular}  
* The bold numbers in the table indicate that in the horizontal direction, the performance of this classifier using this model could achieve the best.  
\end{table} 
Table 5: The availability of transfer learning method based on HAslearning for training to avoid affecting the learning effect of subsequent nodes. The number of classifiers involved in the transfer learning is 22 instead of 27. Furthermore, from the micro perspective of each classifier, for the three metrics of P, R, and F1, the number and proportion of classifiers that use TL and achieve optimal performance in the three models are significantly higher than those of the other two, namely 22 (81%), 21 (78%), and 21 (78%), as shown in Figure 9.  
Moreover, the results show that the XLNet + BiLSTM + HA + FC + TL model is 0.9% higher than that without TL for the P value, 1.1% higher for the R value, and 1% higher for the F1 value.