# A Feature Selection Method for Multi-Label Text Based on Feature Importance
## 4 Experiment and Results
### Experimental Results and Analysis
#### 4.4.2 Performance Comparison Experiment
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Evaluation** & **Algorithm** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** \\ \hline \multirow{4}{*}{AP\(\uparrow\)} & CC+BRKNN & 0.6490 & **0.6673** & **0.6288** & **0.4893** & **0.4527** \\ \cline{2-6}  & DF+BRKNN & **0.6668** & 0.6669 & 0.4925 & 0.4645 & 0.4424 \\ \cline{2-6}  & MI+BRKNN & 0.5605 & 0.5774 & 0.5420 & 0.4385 & 0.4195 \\ \hline \multirow{4}{*}{HL\(\downarrow\)} & CC+BRKNN & 0.0434 & **0.0410** & **0.0418** & **0.0482** & **0.0824** \\ \cline{2-6}  & DF+BRKNN & **0.0411** & 0.0416 & 0.0524 & 0.0807 & 0.0842 \\ \cline{2-6}  & MI+BRKNN & 0.0503 & 0.0499 & 0.0499 & 0.0507 & 0.0816 \\ \hline \multirow{4}{*}{OE\(\downarrow\)} & CC+BRKNN & 0.4311 & 0.3923 & **0.4598** & **0.6324** & **0.6667** \\ \cline{2-6}  & DF+BRKNN & **0.3986** & **0.3897** & 0.6242 & 0.6435 & 0.6684 \\ \cline{2-6}  & MI+BRKNN & 0.5345 & 0.5145 & 0.5808 & 0.6853 & 0.7023 \\ \hline \multirow{4}{*}{CV\(\downarrow\)} & CC+BRKNN & 6.1071 & 5.6158 & **5.0217** & **5.1979** & **5.8342** \\ \cline{2-6}  & DF+BRKNN & **6.0885** & **5.5838** & 5.8472 & 6.9259 & 7.4004 \\ \cline{2-6}  & MI+BRKNN & 7.5864 & 6.7204 & 5.9721 & 6.1377 & 6.0252 \\ \hline \multirow{4}{*}{_RL\(\downarrow\)_} & CC+BRKNN & 0.1227 & 0.1114 & **0.1004** & **0.1106** & **0.1257** \\ \cline{2-6}  & DF+BRKNN & **0.1208** & **0.1107** & 0.1239 & 0.1467 & 0.1586 \\ \cline{2-6}  & MI+BRKNN & 0.1638 & 0.1432 & 0.1276 & 0.1352 & 0.1331 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparison of classification performance on Health data set. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Evaluation** & **Algorithm** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** \\ \hline \multirow{4}{*}{AP\(\uparrow\)} & CC+BRKNN & **0.5939** & **0.6094** & **0.5797** & **0.5408** & **0.5084** \\ \cline{2-6}  & DF+BRKNN & 0.5631 & 0.5611 & 0.5219 & 0.5070 & 0.4894 \\ \cline{2-6}  & MI+BRKNN & 0.4702 & 0.4764 & 0.4687 & 0.4544 & 0.4409 \\ \hline \multirow{4}{*}{HL\(\downarrow\)} & CC+BRKNN & **0.0550** & **0.0562** & **0.0650** & **0.0720** & **0.0768** \\ \cline{2-6}  & DF+BRKNN & 0.0574 & 0.0631 & 0.0724 & 0.0768 & 0.0789 \\ \cline{2-6}  & MI+BRKNN & 0.0648 & 0.0714 & 0.0803 & 0.0844 & 0.0867 \\ \hline \multirow{4}{*}{OE\(\downarrow\)} & CC+BRKNN & **0.5068** & **0.5005** & **0.5585** & **0.6177** & **0.6607** \\ \cline{2-6}  & DF+BRKNN & 0.5482 & 0.5628 & 0.6287 & 0.6580 & 0.6742 \\ \cline{2-6}  & MI+BRKNN & 0.6799 & 0.6914 & 0.7279 & 0.7443 & 0.7555 \\ \hline \multirow{4}{*}{CV\(\downarrow\)} & CC+BRKNN & **4.7235** & **4.3727** & **3.9918** & **4.0888** & **4.2952** \\ \cline{2-6}  & DF+BRKNN & 5.1703 & 4.6991 & 4.4675 & 4.4405 & 4.7396 \\ \cline{2-6}  & MI+BRKNN & 5.7741 & 5.2273 & 4.6481 & 4.5380 & 4.6361 \\ \hline \multirow{4}{*}{_RL\(\downarrow\)_} & CC+BRKNN & **0.1804** & **0.1638** & **0.1499** & **0.1561** & **0.1681** \\ \cline{2-6}  & DF+BRKNN & 0.2004 & 0.1787 & 0.1723 & 0.1730 & 0.1884 \\ \cline{1-1} \cline{2-6}  & MI+BRKNN & 0.2312 & 0.2082 & 0.1857 & 0.1803 & 0.1837 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of classification performance on Entertainment data set. Therefore, the performance of the multi-label text feature selection method based on CC is better than that of the common feature selection methods based on DF and MI. Also, the best values of the evaluation metrics are all obtained in smaller dimensions, which greatly reduces the feature space dimension and improves the classification performance. In summary, it can be seen from the experimental results and analysis on the six data sets that  
1. Compared with the baseline method, the classification performance of the feature selection method proposed in this paper are generally superior in all dimensions, which demonstrates the effectiveness of the proposed method. 2. Good classification performance has been achieved when the proposed method of feature importance is performed on different label assignment methods, demonstrating that the feature importance method based on CC is effective and universal.