# 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  
In this section, we demonstrate the effectiveness of the proposed method by comparing its performance with that of the feature selection method based on DF and the feature selection method based on MI. The classification results on the six data sets in different dimensions using different feature selection methods are shown in Tables 9-14. In these tables, CC denotes the proposed feature selection method, DF denotes the feature selection method based on DF, MI denotes the feature selection method based on MI, CC+BRKNN denotes the multi-label classification performed on the feature space constructed by the proposed method, and BRKNN is used as the classifier. The naming rules for other symbols are the same. Also, we use bold font to denote the best performance in one dimension and underline to denote the best performance in all dimensions. From Table 9 to Table 14, it can be seen that there are 150 comparison results using three feature selection algorithms on six data sets with five evaluation metrics. Among them, the feature selection method based on CC wins 126 times, and the winning percentage is up to 84.0%. Aside from dimensions, the five evaluation metrics have 30 best values on six data sets, 29 of which are obtained in the feature selection method based on CC proposed in this paper. In addition, in the proposed feature selection method, most of the best values of the evaluation metrics are obtained when the dimensions are 10% and 20%, only a few are obtained when the dimension are 30% and 40%, but none when the dimension is 50%. From the perspective of the average precision, compared with the method base on DF, the evaluation metric has the largest increase on the Entertainment data set, which is 8.22%, and compared with the method based on MI, the evaluation metric has the largest increase on the Medical data set, which is 91.65%. \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 & **0.7900** & **0.7786** & 0.7413 & 0.7010 & 0.6770 \\ \cline{2-7}  & DF+BRKNN & 0.7357 & 0.7577 & **0.7438** & **0.7175** & **0.6821** \\ \cline{2-7}  & MI+BRKNN & 0.4122 & 0.3979 & 0.3776 & 0.3521 & 0.3368 \\ \hline \multirow{4}{*}{HL\(\downarrow\)} & CC+BRKNN & **0.0173** & **0.0186** & 0.0203 & 0.0214 & 0.0223 \\ \cline{2-7}  & DF+BRKNN & 0.0198 & 0.0199 & **0.0200** & **0.0207** & **0.0210** \\ \cline{2-7}  & MI+BRKNN & 0.0274 & 0.0275 & 0.0275 & 0.0273 & 0.0275 \\ \hline \multirow{4}{*}{OE\(\downarrow\)} & CC+BRKNN & **0.2802** & **0.3007** & 0.3446 & 0.4099 & 0.4386 \\ \cline{2-7}  & DF+BRKNN & 0.3395 & 0.3252 & **0.3395** & **0.3793** & **0.4191** \\ \cline{2-7}  & MI+BRKNN & 0.7086 & 0.7516 & 0.8221 & 0.7976 & 0.8078 \\ \hline \multirow{4}{*}{CV\(\downarrow\)} & CC+BRKNN & **2.6780** & **2.6681** & **3.2413** & **3.7864** & **4.3058** \\ \cline{2-7}  & DF+BRKNN & 3.8861 & 3.0769 & 3.4942 & 3.8064 & 4.7442 \\ \cline{2-7}  & MI+BRKNN & 7.2564 & 7.3207 & 7.3809 & 8.5993 & 8.6850 \\ \hline \multirow{4}{*}{_RL\(\downarrow\)_} & CC+BRKNN & **0.0452** & **0.0446** & **0.0538** & **0.0658** & **0.0775** \\ \cline{2-7}  & DF+BRKNN & 0.0693 & 0.0499 & 0.0586 & 0.0660 & 0.0851 \\ \cline{1-1} \cline{2-7}  & MI+BRKNN & 0.1372 & 0.1381 & 0.1398 & 0.1706 & 0.1732 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of classification performance on Medical data set.