# Dynamic Online Label Distribution Feature Selection Based on Label Importance and Label Correlation
## 5 Experimental Data
### Experimental Results
From the experimental results in Tables 4-9, we can infer the following:  
1. For all evaluation indicators, DLILC-LDL is generally better than the comparison algorithms. The advantages of Algorithm 1 are basically suitable for all the labeled distribution datasets in the experiment. 2. In terms of the Chebyshev distance and cosine similarity, DLILC-LDL achieved the best performance on nine of the labeled distribution datasets. Note that DLILC-LDL achieved suboptimal or near-suboptimal performance on the remaining dataset. The prediction and classification performance of DLILC-LDL is also very close to the best performance of another comparison algorithm on the labeled distribution dataset. \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 & 0.8108 & 0.7718 & 0.8413 & 0.8359 & 0.8163 & **0.7368** & 0.7564 \\ Natural\_Scene & 5.7448 & 5.8124 & 5.7120 & **5.6357** & 5.7524 & 5.7997 & 5.6653 \\ Human\_Gene & 16.8239 & 16.3366 & 16.3188 & 16.3464 & 16.3496 & 16.3495 & **16.2999** \\ Yeast-alpha (alpha) & 0.6781 & **0.6837** & 0.6836 & 0.6860 & 0.6847 & 0.6872 & 0.6841 \\ Yeast-diau (diau) & 0.4135 & 0.4138 & **0.4087** & 0.4103 & 0.4103 & 0.4109 & 0.4014 \\ Yeast-dtt (dtt) & 0.1887 & 0.1885 & 0.1885 & 0.1890 & 0.1888 & 0.1880 & **0.1866** \\ Yeast-elu (elu) & 0.6104 & 0.6072 & 0.6106 & 0.6091 & 0.6115 & 0.6163 & **0.6055** \\ Yeast-heat (heat) & 0.3742 & 0.3751 & 0.3758 & 0.3753 & 0.3770 & 0.3740 & **0.3720** \\ Yeast-spo (spo) & 0.5251 & 0.5258 & 0.5250 & 0.5246 & 0.5259 & 0.5233 & **0.5176** \\ Yeast-spo5 (spo5) & 0.2763 & 0.2757 & 0.2773 & 0.2766 & 0.2765 & **0.2738** & 0.2751 \\ avg & 2.6446 & 2.5991 & 2.5942 & 2.5889 & 2.5993 & 2.5959 & **2.5764** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the Canberra distance results of different algorithms \(\downarrow\). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **MDDMproj** & **MDDMspc** & **MDFS** & **LDFS** & **GRRO** & **FSFL** & **DLILC-LDL** \\ \hline S-JAFFE & 0.06453 & 0.06130 & 0.06767 & 0.06957 & 0.06657 & 0.04857 & **0.04340** \\ Natural\_Scene & 0.7070 & 0.7387 & **0.6850** & 0.6489 & 0.7028 & 0.7058 & 0.6468 \\ Human\_Gene & 0.6394 & 0.6063 & 0.6071 & 0.6138 & 0.6014 & 0.6510 & **0.5862** \\ Yeast-alpha (alpha) & 0.005350 & 0.005417 & 0.005413 & 0.005447 & 0.005413 & 0.005510 & **0.005403** \\ Yeast-diau (diau) & 0.01168 & 0.01166 & 0.01166 & 0.01174 & 0.01176 & **0.01154** & 0.01168 \\ Yeast-dtt (dtt) & 0.006878 & 0.006900 & 0.006882 & 0.006960 & 0.006900 & 0.006838 & **0.006716** \\ Yeast-elu (elu) & 0.006867 & 0.006833 & 0.006700 & 0.006750 & 0.006775 & 0.006875 & **0.006625** \\ Yeast-heat (heat) & 0.01323 & 0.01325 & 0.01320 & 0.01330 & 0.01335 & 0.01345 & **0.01305** \\ Yeast-spo (spo) & 0.02470 & 0.02478 & 0.02456 & 0.02458 & 0.02472 & 0.02456 & **0.02396** \\ Yeast-spo5 (spo5) & 0.02965 & 0.02960 & 0.02968 & 0.02988 & 0.02998 & 0.02993 & **0.02923** \\ avg & _0.1509_ & _0.1505_ & _0.1458_ & _0.1431_ & _0.1470_ & _0.1504_ & **0.1373** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the KL divergence results of different algorithms \(\downarrow\).