# Dynamic Online Label Distribution Feature Selection Based on Label Importance and Label Correlation
## 5 Experimental Data
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **MDDMproj** & **MDDMspc** & **MDFS** & **LDFS** & **GRRO** & **FSFL** & **DLILC-LDL** \\ \hline S-JAFFE & 0.9421 & 0.9442 & 0.9372 & 0.9361 & 0.9389 & 0.9540 & **0.9614** \\ Natural\_Scene & 0.7586 & 0.7519 & 0.7667 & 0.7745 & 0.7627 & 0.7604 & **0.7753** \\ Human\_Gene & 0.7437 & 0.7412 & 0.7399 & 0.7347 & 0.7421 & 0.7440 & **0.7458** \\ Yeast-alpha (alpha) & **0.9948** & **0.9948** & 0.9946 & 0.9947 & 0.9947 & 0.9946 & 0.9947 \\ Yeast-diau (diau) & 0.9887 & 0.9887 & 0.9889 & 0.9888 & 0.9888 & 0.9887 & **0.9892** \\ Yeast-dtt (dtt) & 0.9926 & 0.9926 & 0.9927 & 0.9926 & 0.9925 & 0.9928 & **0.9928** \\ Yeast-elu (elu) & 0.9936 & 0.9936 & 0.9936 & 0.9935 & 0.9935 & **0.9937** & 0.9936 \\ Yeast-heat (heat) & 0.9799 & 0.9873 & 0.9873 & 0.9872 & 0.9872 & 0.9874 & **0.9875** \\ Yeast-spo (spo) & 0.9763 & 0.9766 & 0.9762 & 0.9762 & 0.9762 & 0.9764 & **0.9769** \\ Yeast-spo5 (spo5) & 0.9742 & 0.9743 & 0.9744 & **0.9748** & 0.9739 & 0.9741 & 0.9746 \\ avg & _0.9344_ & _0.9346_ & _0.9351_ & _0.9352_ & _0.9350_ & _0.9366_ & **0.9392** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of the cosine similarity results of different algorithms \(\uparrow\). In particular, in terms of the Chebyshev distance, the DLILC-LDL algorithm generally achieved the best performance. 3. In terms of the Clark and Canberra distances, the prediction and classification performance results of DLILC-LDL are significantly better than those of the compared algorithms on seven label distribution datasets. Note that even on the datasets where the best performance was not achieved, the results of DLILC-LDL are still quite good, and it ranked second on the remaining datasets. 4. In terms of different evaluation indicators, the average classification performance of DLILC-LDL is significantly better than that of all the compared algorithms. These experimental results reveal that the DLILC-LDL has better performance than the other compared algorithms. Prediction performance is expected to vary. To clearly evaluate the differences among the algorithms, the prediction performance was normalized to [0.1, 0.5] following [23]. Figure 6 shows the normalized stability indices for the Chebyshev distance, Clark distance, Canberra distance, KL divergence, cosine similarity, and intersection similarity. Each corner of the spider-web chart in Figure 6 represents a different dataset, and each colored line represents a different algorithm. If the area of the graph enclosed by a specific line is large and its shape is similar to that of a regular dodecagon, the performance and stability of the corresponding algorithm are good. A stability value of approximately 0.5 is considered to be a good value. Figure 6 reveals the following:  
Figure 6: Spider-web charts for stability analysis. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **MDDMproj** & **MDDMspc** & **MDFS** & **LDFS** & **GRRO** & **FSFL** & **DLILC-LDL** \\ \hline S-JAFFE & 0.8661 & 0.8680 & 0.8677 & 0.8626 & 0.8670 & 0.8804 & **0.8825** \\ Natural\_Scene & 0.5297 & 0.5195 & 0.5472 & **0.5679** & 0.5403 & 0.5391 & 0.5633 \\ Human\_Gene & 0.7323 & 0.7329 & 0.7329 & 0.7323 & 0.7321 & 0.7322 & **0.7330** \\ Yeast-alpha (alpha) & 0.9621 & 0.9621 & 0.9621 & 0.9620 & 0.9621 & **0.9624** & 0.9622 \\ Yeast-diau (diau) & 0.9423 & 0.9422 & 0.9427 & 0.9426 & 0.9426 & 0.9425 & **0.9438** \\ Yeast-dtt (dtt) & 0.9542 & 0.9542 & 0.9544 & 0.9547 & 0.9544 & 0.9546 & **0.9550** \\ Yeast-elu (elu) & 0.9568 & 0.9570 & 0.9568 & 0.9569 & 0.9567 & 0.9564 & **0.9572** \\ Yeast-heat (heat) & 0.9383 & 0.9382 & 0.9371 & 0.9383 & 0.9379 & 0.9378 & **0.9388** \\ Yeast-spo (spo) & 0.9135 & 0.9134 & 0.9135 & 0.9134 & 0.9132 & **0.9171** & 0.9146 \\ Yeast-spo5 (spo5) & 0.9107 & 0.9109 & 0.9108 & 0.9106 & 0.9106 & 0.9104 & **0.9119** \\ avg & _0.8706_ & _0.8699_ & _0.8725_ & _0.8741_ & _0.87179_ & _0.8732_ & _0.8762_ \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of the intersection similarity results of different algorithms \(\uparrow\).