# A Feature Selection Method for Multi-Label Text Based on Feature Importance
## 5 Conclusions  
Aiming at the high dimensionality and sparsity of text feature space, a multi-label text feature selection method was proposed in this paper. Firstly, the label assignment method was used to transformed multi-label texts into single-label texts. Then, on this basis, an importance method based on CC was proposed to calculate the importance of each feature. Finally, features with higher importance were selected to construct the feature space. In the proposed method, the multi-label feature selection problem has been transformed into a single-label one. Thus, the feature selection process is simple and fast, and the dimension reduction effect is obvious. The proposed method is very suitable for high-dimensional text data. Compared with the baseline method and the commonly used  
\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 & **0.7236** & **0.7008** & **0.6610** & **0.6944** & **0.6923** \\ \cline{2-6}  & DF+BRKNN & 0.7081 & 0.6724 & 0.6097 & 0.6863 & 0.6912 \\ \cline{2-6}  & MI+BRKNN & 0.5817 & 0.5924 & 0.5906 & 0.5421 & 0.6016 \\ \hline \multirow{4}{*}{HL\(\downarrow\)} & CC+BRKNN & **0.0223** & **0.0237** & **0.0275** & **0.0237** & 0.0242 \\ \cline{2-6}  & DF+BRKNN & 0.0226 & 0.0271 & 0.0276 & 0.0238 & **0.0232** \\ \cline{2-6}  & MI+BRKNN & 0.0310 & 0.0312 & 0.0313 & 0.0321 & 0.0308 \\ \hline \multirow{4}{*}{OE\(\downarrow\)} & CC+BRKNN & **0.3414** & **0.3796** & **0.4398** & **0.3937** & 0.4068 \\ \cline{2-6}  & DF+BRKNN & 0.3525 & 0.4203 & 0.5522 & 0.4043 & **0.3916** \\ \cline{2-6}  & MI+BRKNN & 0.5355 & 0.5464 & 0.5385 & 0.6141 & 0.5044 \\ \hline \multirow{4}{*}{CV\(\downarrow\)} & CC+BRKNN & **5.2504** & **4.8211** & **5.0733** & **5.0137** & **4.8636** \\ \cline{2-6}  & DF+BRKNN & 5.8012 & 4.9990 & 5.8213 & 5.4352 & 5.9549 \\ \cline{2-6}  & MI+BRKNN & 7.4690 & 6.2813 & 6.4551 & 8.1137 & 8.3188 \\ \hline \multirow{4}{*}{\(RL\downarrow\)} & CC+BRKNN & **0.0997** & **0.0926** & **0.0993** & **0.0965** & **0.0939** \\ \cline{2-6}  & DF+BRKNN & 0.1118 & 0.0969 & 0.1154 & 0.1063 & 0.1180 \\ \cline{1-1} \cline{2-6}  & MI+BRKNN & 0.1549 & 0.1286 & 0.1287 & 0.1675 & 0.1732 \\ \hline \hline \end{tabular}
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Table 14: Comparison of classification performance on Social data set.  
feature selection methods, the proposed feature selection methods all achieved better performance on the six public data sets, which demonstrates the effectiveness of the method.  
In this paper, a method of feature importance based on CC is proposed from the perspective of category. The contribution of features to classification of different categories was calculated from two aspects of inter-category and intra-category, clarifying the importance of features to different categories, and selecting features with strong category classification ability. The proposed method has a good performance.  
However, the proposed algorithm does not consider the correlation between categories, which should be studied in the future.  
[PERSON] and [PERSON] conceived the algorithm and designed the experiments. [PERSON] implemented the experiments, analyzed the results and wrote the paper. [PERSON] revised the clarity of the work as well as helping to write and organize the paper. All authors read and approved the final manuscript.  
This research was supported by the Agricultural Finance Project under Grant 051821301112421014, the Provincial-School Cooperation Project under Grant 201704070, and the National High Technology Research and Development Program of China (863 Program) under Grant 2013 AA102306.  
We are deeply grateful to the reviewers and the editors for their valuable comments and suggestions, which improved the technical content and presentation of the paper.  
The authors declare no conflicts of interest.