# 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 & **0.8482** & **0.8611** & **0.8692** & **0.8729** & **0.8696** \\ \cline{2-6}  & DF+BRKNN & 0.8460 & 0.8541 & 0.8567 & 0.8606 & 0.8561 \\ \cline{2-6}  & MI+BRKNN & 0.8330 & 0.8362 & 0.8424 & 0.8453 & 0.8433 \\ \hline \multirow{4}{*}{HL\(\downarrow\)} & CC+BRKNN & 0.0287 & **0.0268** & **0.0255** & **0.0254** & **0.0260** \\ \cline{2-6}  & DF+BRKNN & **0.0283** & 0.0276 & 0.0275 & 0.0266 & 0.0273 \\ \cline{2-6}  & MI+BRKNN & 0.0293 & 0.0289 & 0.0288 & 0.0285 & 0.0284 \\ \hline \multirow{4}{*}{OE\(\downarrow\)} & CC+BRKNN & **0.1298** & **0.1156** & **0.1055** & **0.1001** & **0.1073** \\ \cline{2-6}  & DF+BRKNN & 0.1309 & 0.1253 & 0.1218 & 0.1130 & 0.1188 \\ \cline{2-6}  & MI+BRKNN & 0.1342 & 0.1317 & 0.1314 & 0.1309 & 0.1308 \\ \hline \multirow{4}{*}{CV\(\downarrow\)} & CC+BRKNN & **4.1161** & **3.9341** & **3.6375** & **3.4179** & **3.6064** \\ \cline{2-6}  & DF+BRKNN & 4.2224 & 3.9910 & 3.9218 & 4.1443 & 4.3527 \\ \cline{2-6}  & MI+BRKNN & 4.7304 & 4.6595 & 4.1774 & 3.9514 & 4.3903 \\ \hline \multirow{4}{*}{\(RL\)\(\downarrow\)} & CC+BRKNN & **0.0756** & **0.0696** & **0.0627** & **0.0580** & **0.0632** \\ \cline{2-6}  & DF+BRKNN & 0.0781 & 0.0719 & 0.0700 & 0.0718 & 0.0755 \\ \cline{2-6}  & MI+BRKNN & 0.0912 & 0.0882 & 0.0782 & 0.0719 & 0.0810 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of classification performance on Business data set. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation** & **Algorithm** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** \\ \hline \multirow{4}{*}{AP\(\uparrow\)} & CC+BRKNN & 0.6290 & **0.6633** & **0.6558** & **0.6448** & **0.6354** \\ \cline{2-6}  & DF+BRKNN & **0.6352** & 0.6532 & 0.6480 & 0.6391 & 0.6314 \\ \cline{2-6}  & MI+BRKNN & 0.5900 & 0.6093 & 0.6148 & 0.6135 & 0.6171 \\ \hline \multirow{4}{*}{HL\(\downarrow\)} & CC+BRKNN & 0.0373 & **0.0355** & **0.0374** & **0.0389** & **0.0396** \\ \cline{2-6}  & DF+BRKNN & **0.0360** & 0.0363 & 0.0382 & 0.0395 & 0.0399 \\ \cline{2-6}  & MI+BRKNN & 0.0407 & 0.0401 & 0.0409 & 0.0416 & 0.0415 \\ \hline \multirow{4}{*}{OE\(\downarrow\)} & CC+BRKNN & 0.4327 & **0.3916** & **0.4005** & **0.4159** & **0.4197** \\ \cline{2-6}  & DF+BRKNN & **0.4149** & 0.3991 & 0.4093 & 0.4203 & 0.4253 \\ \cline{2-6}  & MI+BRKNN & 0.4699 & 0.4491 & 0.4446 & 0.4483 & 0.4435 \\ \hline \multirow{4}{*}{CV\(\downarrow\)} & CC+BRKNN & **6.6824** & **5.6804** & **5.0189** & **4.8361** & **4.9038** \\ \cline{2-6}  & DF+BRKNN & 6.7307 & 5.7193 & 5.0377 & 4.8693 & 4.9880 \\ \cline{2-6}  & MI+BRKNN & 7.5324 & 6.5456 & 5.8639 & 5.5300 & 5.4504 \\ \hline \multirow{4}{*}{\(RL\)\(\downarrow\)} & CC+BRKNN & **0.1472** & 0.1231 & **0.1080** & **0.1039** & **0.1066** \\ \cline{2-6}  & DF+BRKNN & 0.1476 & **0.1230** & **0.1080** & 0.1042 & 0.1081 \\ \cline{1-1} \cline{2-6}  & MI+BRKNN & 0.1682 & 0.1454 & 0.1283 & 0.1204 & 0.1184 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of classification performance on Computers data set.