# Dynamic Online Label Distribution Feature Selection Based on Label Importance and Label Correlation
## 6 Summary  
This paper proposed a label distribution feature selection model based on label correlation and NRS, and it further proposed a label distribution feature selection algorithm based on this model, namely, a dynamic algorithm for processing streaming features. In the proposed approach, we first defined a new neighborhood particle by averaging the nearest neighbor method to better connect the information between the features of the label distribution data. Then, by calculating the mutual information between the features, we obtained the feature correlation weight and combined the lower approximation of the new neighborhood with the feature weight to obtain a new feature subset importance model. On this basis, we generalized the traditional NRS model, defined a new label space \begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation Metric** & \(\mathbf{F_{F}}\) & **Critical Value (\(\mathbf{\alpha=0.10}\))** \\ \hline Chebyshev & 6.357 & 1.770 \\ Clark & 6.294 & \\ Canberra & 5.286 & \\ KL & 8.450 & \\ Cosine & 5.269 & \\ Intersection & 6.683 & \\ \hline \hline \end{tabular}  
From Figure 7, we can observe the following:  
1. DLILC-LDL shows significant improvement in several evaluation indicators. DLILC-LDL significantly outperforms other comparison algorithms. The relative advantage is obvious.
2. Although the classification performance results of FSFL and GRRO are comparable, the average classification performance of DLILC-LDL in Tables 3-8 is significantly better than that of the comparison algorithms. In summary, DLILC-LDL is better than other advanced multi-label feature selection algorithms, and this method has stronger statistical performance.  
\end{table} 
Table 10: Summary of the Friedman test ( \(\mathrm{k=8,\ N=10}\) ), giving the \(F_{F}\) values and critical values of each evaluation index on \(\alpha=0.10\). Figure 7: Bonferroni–Dunn comparison of SLILC-LDL and DLILC-LDL with the comparison algorithms. granularity, and granulated the label space of the label distribution data to adapt it to LDL. Incorporating rough set theory, we defined the label distribution neighborhood decision system and proposed a new importance model. Moreover, based on this algorithm, we proposed a dynamic feature selection algorithm that can solve the label distribution streaming feature problem by testing the arriving features in the time series using importance, significance, and redundancy analyses. The experimental results show that our algorithm is highly competitive compared with other commonly used algorithms. However, in a large number of dynamic data environments, the practical application of this method is somewhat challenging due to the high time complexity of the algorithm. The time complexity of the algorithm mainly comes from the calculation of the sample neighborhood and the feature selection of the sample based on the neighborhood. In the labeled distribution environment, the label granularity of the sample also needs to be calculated. Therefore, in order to improve the time complexity, the samples can be pre-sorted according to the rules of the label space, and the correctness of the feature selection can be further improved. This will be the direction for research and optimization in the next step. Therefore, in future work, we hope to further optimize the algorithm and reduce the running time of the algorithm. At the same time, we hope to further improve the label space particles and propose a better label distribution neighborhood decision system. In addition, the next step is to combine this method with practical applications (such as sentiment computing and multimodal sentiment recognition) to practice this method. Conceptualization, W.C.; Methodology, W.C.; Software, W.C.; Validation, W.C.; Writing--original draft, W.C.; Writing--review & editing, X.S. and F.R. ; Supervision, F.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.