# A Feature Selection Method for Multi-Label Text Based on Feature Importance
## 4 Experiment and Results
### Experimental Results and Analysis  
#### 4.4.1 Proposed Algorithm Validation Experiment  
The classification results on the six public data sets in different dimensions of average precision, hamming loss, one error, coverage and ranking loss are shown in Figures 2-7. In these figures, the horizontal axis denotes the proportion of the selected features, that is, the horizontal axis denotes the value of \(t\), and the vertical axis denotes the value of the evaluation metric; CC+BRKNN denotes that the multi-label classification is performed on the feature space constructed by the proposed method, and BRKNN is used as the classifier; CC+MLKNN denotes that the multi-label classification is performed on the feature space constructed by the proposed method, and MLKNN is used as the classifier; BaseLine+BRKNN denotes that the multi-label classification is performed on the original feature space, and BRKNN is used as the classifier; and BaseLine+MLKNN denotes that the multi-label classification is performed on the original feature space, and MLKNN is used as the classifier. Figure 2: Experimental results on Medical data set. (**a**) Average Precision; (**b**) Hamming Loss; (**c**) One Error; (**d**) Coverage; (**e**) Ranking Loss. Figure 4: _Cont._  
Figure 3: Experimental results on Business data set. (**a**) Average Precision; (**b**) Hamming Loss; (**c**) One Error; (**d**) Coverage; (**e**) Ranking Loss. Figure 4: Experimental results on Computers data set. (**a**) Average Precision; (**b**) Hamming Loss; (**c**) One Error; (**d**) Coverage; (**e**) Ranking Loss. Figure 5: Experimental results on Entertainment data set. (**a**) Average Precision; (**b**) Hamming Loss; (**c**) One Error; (**d**) Coverage; (**e**) Ranking Loss. It can be seen from Figure 2 to Figure 7 that, on the six data sets, the classifications performed on the feature spaces constructed by the proposed feature selection method universally have better performance than that of the baseline method in each dimension. In classification experiments, the average precision is the most intuitive and concerned evaluation metric. Therefore, we regard the best value of the average precision as the best performance of the classification, so as to analyze the experimental results. Compared with the classification results in the original feature space, the increase (decrease) percentage of each evaluation metric is shown in Table 2.  
Figure 6: Experimental results on Health data set. (**a**) Average Precision; (**b**) Hamming Loss; (**c**) One Error; (**d**) Coverage; (**e**) Ranking Loss. Figure 7: _Cont._  
From Table 2, it can be seen that on the six data sets, most of the classification evaluation metrics obtained in the feature space constructed by the proposed method are universally better than those obtained in the original feature space. Only on the Computers data set, when the value of \(t\) is 20% and BRKNN is used as the classifier, the coverage and ranking loss obtained in the feature space constructed by the proposed method are worse than those obtained in the original feature space. Excitingly, on the Health data set, when the value of \(t\) is 20% and BRKNN is used as the classifier, the average precision is increased by 67.79%, which is the greatest increase among all. In order to further demonstrate the effectiveness of the proposed feature importance method based on CC, we experimented it on ALA, NLA, HLA, and SLA. The experimental results of the six public data sets are shown in Tables 3-8. In these tables, BaseLine denotes the multi-label classifications performed on the original feature space; 10%, 20%, 30%, 40%, and 50% denote the multi-label classifications performed on the feature spaces in different