# Improving Multi-Label Learning by Correlation Embedding
## 4 Experiment
### Evaluation Metrics
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multicolumn{6}{c}{**AUC \(\uparrow\)**} \\ \cline{2-9}  & **MLLCE** & **NL-MLLCE** & **ML-LSS** & **JFSC** & **BR** & **Glocal** & **MLL-FLSDR** \\ \hline rcv1 subset1 & 0.941 \(\pm\) 0.002 & **0.942**\(\pm\) 0.003 & 0.928 \(\pm\) 0.002 & 0.921 \(\pm\) 0.003 & 0.940 \(\pm\) 0.004 & 0.925 \(\pm\) 0.002 & 0.930 \(\pm\) 0.004 \\ rcv1 subset2 & 0.936 \(\pm\) 0.002 & **0.938**\(\pm\) 0.002 & 0.925 \(\pm\) 0.002 & 0.919 \(\pm\) 0.004 & 0.910 \(\pm\) 0.006 & 0.920 \(\pm\) 0.003 & 0.918 \(\pm\) 0.006 \\ enron & 0.911 \(\pm\) 0.001 & **0.913**\(\pm\) 0.004 & 0.904 \(\pm\) 0.005 & 0.890 \(\pm\) 0.009 & 0.908 \(\pm\) 0.004 & 0.882 \(\pm\) 0.002 & 0.908 \(\pm\) 0.004 \\ recreation & 0.813 \(\pm\) 0.009 & **0.826**\(\pm\) 0.010 & 0.824 \(\pm\) 0.010 & 0.821 \(\pm\) 0.009 & 0.723 \(\pm\) 0.005 & 0.820 \(\pm\) 0.005 & 0.813 \(\pm\) 0.005 \\ stackex-coffee & **0.853**\(\pm\) 0.027 & 0.841 \(\pm\) 0.019 & 0.763 \(\pm\) 0.035 & 0.794 \(\pm\) 0.030 & 0.810 \(\pm\) 0.042 & 0.836 \(\pm\) 0.022 & 0.781 \(\pm\) 0.042 \\ stackex-ches & 0.876 \(\pm\) 0.009 & **0.903**\(\pm\) 0.009 & 0.884 \(\pm\) 0.005 & 0.883 \(\pm\) 0.010 & 0.881 \(\pm\) 0.007 & 0.866 \(\pm\) 0.011 & 0.877 \(\pm\) 0.035 \\ stackex-philisophy & 0.881 \(\pm\) 0.008 & **0.888**\(\pm\) 0.009 & 0.874 \(\pm\) 0.004 & 0.879 \(\pm\) 0.003 & 0.870 \(\pm\) 0.005 & 0.845 \(\pm\) 0.001 & 0.884 \(\pm\) 0.002 \\ stackex-chemistry & 0.877 \(\pm\) 0.003 & 0.886 \(\pm\) 0.002 & 0.888 \(\pm\) 0.004 & **0.892**\(\pm\) 0.003 & 0.846 \(\pm\) 0.004 & 0.866 \(\pm\) 0.004 & 0.887 \(\pm\) 0.006 \\ stackex-cs & 0.925 \(\pm\) 0.003 & **0.927**\(\pm\) 0.002 & 0.923 \(\pm\) 0.003 & 0.922 \(\pm\) 0.002 & 0.883 \(\pm\) 0.006 & 0.902 \(\pm\) 0.003 & 0.917 \(\pm\) 0.002 \\ stackex-cooking & **0.900**\(\pm\) 0.002 & 0.899 \(\pm\) 0.003 & 0.894 \(\pm\) 0.005 & 0.892 \(\pm\) 0.004 & 0.863 \(\pm\) 0.004 & 0.892 \(\pm\) 0.002 & 0.895 \(\pm\) 0.002 \\ corel16k001 & 0.850 \(\pm\) 0.003 & 0.845 \(\pm\) 0.003 & 0.838 \(\pm\) 0.004 & 0.837 \(\pm\) 0.003 & 0.831 \(\pm\) 0.002 & 0.825 \(\pm\) 0.007 & 0.834 \(\pm\) 0.002 \\ corel16k002 & 0.839 \(\pm\) 0.004 & **0.851**\(\pm\) 0.003 & 0.847 \(\pm\) 0.002 & 0.846 \(\pm\) 0.003 & 0.824 \(\pm\) 0.003 & 0.828 \(\pm\) 0.003 & 0.839 \(\pm\) 0.001 \\ water-quality & 0.699 \(\pm\) 0.005 & 0.697 \(\pm\) 0.008 & 0.694 \(\pm\) 0.007 & **0.702**\(\pm\) 0.011 & 0.684 \(\pm\) 0.007 & 0.691 \(\pm\) 0.005 & 0.664 \(\pm\) 0.012 \\ flags & 0.745 \(\pm\) 0.031 & 0.748 \(\pm\) 0.025 & **0.751**\(\pm\) 0.017 & 0.736 \(\pm\) 0.014 & 0.743 \(\pm\) 0.032 & 0.742 \(\pm\) 0.020 & 0.744 \(\pm\) 0.037 \\ delicious & 0.859 \(\pm\) 0.002 & 0.881 \(\pm\) 0.001 & 0.884 \(\pm\) 0.002 & **0.886**\(\pm\) 0.001 & 0.882 \(\pm\) 0.001 & 0.848 \(\pm\) 0.001 & 0.877 \(\pm\) 0.090 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The experimental results (mean \(\pm\) standard) of all comparison methods in this paper in terms of AUC. \(\uparrow\) means that the larger the value, the better the performance is. The best results in each row are highlighted in bold face.