# Dynamic Online Label Distribution Feature Selection Based on Label Importance and Label Correlation
## 5 Experimental Data
### Experimental Results  
To demonstrate the effectiveness of our algorithm, we compared the algorithm proposed in this paper, the DLILC-LDL algorithms, with MDDMproj, MDDMspc, MDFS, LDFS, GRRO, and FSF in terms of their predictive classification performance. To ensure the results were comparable, all features obtained by all comparison algorithms were sorted, the number of features selected by the dynamic feature selection method proposed in this paper was used as the final feature subset, and the features with the same number of features in the comparison algorithm results were used as their feature subsets. Because all the comparison algorithms use the result of feature selection as the result of feature sorting, we give the detailed experimental results of all algorithms on the various classification datasets in Tables 4-9. For each evaluation criterion, \"\(\downarrow\)\" indicates that smaller values are better and \"\(\uparrow\)\" indicates that larger values are better. In addition, the best predictive classification performance of the evaluation indicator is shown in bold and the average predictive classification performance of each algorithm is shown in italics. In addition, in our experiments, to study the performance of the streaming feature selection method when the entire feature set is unknown in advance, we used the 12 benchmark datasets listed in Table 2 as our testbed and simulated the streaming feature situation by observing the features on the training data that arrived each time. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **MDDMproj** & **MDDMspc** & **MDFS** & **LDFS** & **GRRO** & **FSFL** & **DILC-LDL** \\ \hline S-JAFFE & 0.1029 & 0.1013 & 0.1087 & 0.1099 & 0.1054 & 0.0860 & **0.0832** \\ Natural\_Scene & 0.3431 & 0.3395 & 0.3336 & 0.3266 & 0.3323 & 0.3497 & **0.3199** \\ Human\_Gene & 0.06303 & 0.06310 & 0.06304 & 0.06312 & 0.06320 & 0.06313 & **0.06291** \\ Yeast-alpha (alpha) & 0.009805 & 0.009863 & 0.009910 & 0.009883 & 0.009835 & 0.009878 & **0.009775** \\ Yeast-diau (diau) & 0.03600 & 0.03604 & 0.03558 & 0.03564 & 0.03558 & 0.03576 & **0.03456** \\ Yeast-dtt (dtt) & 0.04020 & 0.03998 & 0.03974 & **0.03815** & 0.03990 & 0.03973 & 0.03939 \\ Yeast-elu (elu) & 0.01690 & 0.01700 & 0.01688 & 0.01680 & 0.01688 & 0.01690 & **0.01668** \\ Yeast-heat (heat) & 0.04363 & 0.04331 & 0.04343 & 0.04338 & 0.04335 & 0.04298 & **0.04282** \\ Yeast-spo (spo) & 0.05936 & 0.05948 & 0.05940 & 0.05946 & 0.05942 & 0.05918 & **0.05852** \\ Yeast-spo5 (spo5) & 0.09360 & 0.09243 & 0.09338 & 0.09265 & 0.09245 & 0.09343 & **0.09168** \\ Avg & _0.08085_ & _0.080020_ & _0.08036_ & _0.07955_ & _0.07983_ & _0.07967_ & _0.07595_ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the Chebyshev distance results of different algorithms \(\downarrow\). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **MDDMproj** & **MDDMspc** & **MDFS** & **LDFS** & **GRRO** & **FSFL** & **DILC-LDL** \\ \hline S-JAFFE & 0.3776 & 0.3546 & 0.3827 & 0.3979 & 0.3795 & 0.3398 & **0.3369** \\ Natural\_Scene & 2.0478 & 2.0607 & 2.0347 & **2.0114** & 2.0491 & 2.0463 & 2.0216 \\ Human\_Gene & 1.4321 & 1.4602 & 1.4548 & 1.4397 & 1.4425 & 1.4423 & **1.4090** \\ Yeast-alpha (alpha) & 0.2234 & 0.2213 & 0.2086 & 0.2087 & 0.2083 & 0.2094 & **0.2073** \\ Yeast-diau(diau) & 0.1900 & 0.1908 & 0.1886 & 0.1892 & 0.1890 & 0.1899 & **0.1850** \\ Yeast-dtt (dtt) & 0.1126 & 0.1125 & 0.1123 & 0.1416 & 0.1122 & 0.1118 & **0.1106** \\ Yeast-elu (elu) & 0.2064 & 0.2060 & **0.2059** & 0.2066 & 0.2069 & 0.2082 & 0.2060 \\ Yeast-heat (heat) & 0.1453 & 0.1454 & 0.1458 & 0.1462 & **0.1440** & 0.1456 \\ Yeast-spo (spo) & **0.1986** & 0.2101 & 0.2102 & 0.2104 & 0.2106 & 0.2104 & 0.2075 \\ Yeast-spo5 (spo5) & 0.1809 & 0.1796 & 0.1798 & 0.1803 & 0.1804 & 0.1804 & **0.1775** \\ Avg & _0.5115_ & _0.5141_ & _0.5123_ & _0.5131_ & _0.5125_ & _0.5082_ & _0.5007_ \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the Clark distance results of different algorithms \(\downarrow\).