# 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{8}{c}{**Micro F1 \(\uparrow\)**} \\ \cline{2-7}  & **MLLCE** & **NL-MLLCE** & **ML-LSS** & **JFSC** & **BR** & **Glocal** & **MLL-FLSDR** \\ \hline rcv1 subset1 & 0.313 \(\pm\) 0.008 & 0.315 \(\pm\) 0.007 & 0.353 \(\pm\) 0.005 & 0.316 \(\pm\) 0.009 & 0.286 \(\pm\) 0.009 & **0.354**\(\pm\) 0.008 & 0.328 \(\pm\) 0.011 \\ rcv1 subset2 & 0.302 \(\pm\) 0.011 & 0.299 \(\pm\) 0.006 & **0.368**\(\pm\) 0.009 & 0.351 \(\pm\) 0.012 & 0.333 \(\pm\) 0.016 & 0.357 \(\pm\) 0.006 & 0.303 \(\pm\) 0.022 \\ enron & 0.525 \(\pm\) 0.006 & 0.530 \(\pm\) 0.019 & 0.526 \(\pm\) 0.016 & 0.553 \(\pm\) 0.018 & 0.573 \(\pm\) 0.010 & 0.506 \(\pm\) 0.021 & **0.577**\(\pm\) 0.019 \\ recreation & **0.373**\(\pm\) 0.007 & 0.348 \(\pm\) 0.010 & 0.350 \(\pm\) 0.015 & 0.333 \(\pm\) 0.018 & 0.365 \(\pm\) 0.010 & 0.053 \(\pm\) 0.010 & 0.345 \(\pm\) 0.013 \\ stackex-coffee & 0.158 \(\pm\) 0.065 & 0.161 \(\pm\) 0.031 & 0.155 \(\pm\) 0.028 & 0.087 \(\pm\) 0.052 & 0.009 \(\pm\) 0.011 & **0.231**\(\pm\) 0.060 & 0.066 \(\pm\) 0.041 \\ stackex-ches & **0.314**\(\pm\) 0.003 & 0.238 \(\pm\) 0.023 & 0.274 \(\pm\) 0.017 & 0.207 \(\pm\) 0.011 & 0.248 \(\pm\) 0.011 & 0.110 \(\pm\) 0.014 & 0.251 \(\pm\) 0.023 \\ stackex-philisophy & 0.271 \(\pm\) 0.007 & 0.247 \(\pm\) 0.007 & **0.298**\(\pm\) 0.009 & 0.227 \(\pm\) 0.007 & 0.256 \(\pm\) 0.009 & 0.071 \(\pm\) 0.006 & 0.242 \(\pm\) 0.017 \\ stackex-chemistry & **0.192**\(\pm\) 0.006 & 0.190 \(\pm\) 0.011 & 0.190 \(\pm\) 0.011 & 0.141 \(\pm\) 0.006 & 0.166 \(\pm\) 0.009 & 0.138 \(\pm\) 0.009 & 0.157 \(\pm\) 0.008 \\ stackex-cs & 0.296 \(\pm\) 0.005 & 0.299 \(\pm\) 0.005 & **0.301**\(\pm\) 0.009 & 0.216 \(\pm\) 0.007 & 0.257 \(\pm\) 0.009 & 0.220 \(\pm\) 0.014 & 0.256 \(\pm\) 0.011 \\ stackex-cooking & 0.284 \(\pm\) 0.007 & 0.317 \(\pm\) 0.007 & **0.324**\(\pm\) 0.006 & 0.247 \(\pm\) 0.008 & 0.290 \(\pm\) 0.007 & 0.181 \(\pm\) 0.013 & 0.297 \(\pm\) 0.004 \\ corel16k001 & 0.044 \(\pm\) 0.003 & 0.051 \(\pm\) 0.002 & 0.064 \(\pm\) 0.003 & **0.076**\(\pm\) 0.003 & 0.064 \(\pm\) 0.003 & 0.068 \(\pm\) 0.001 & 0.057 \(\pm\) 0.003 \\ corel16k002 & 0.065 \(\pm\) 0.002 & 0.053 \(\pm\) 0.006 & 0.095 \(\pm\) 0.005 & **0.080**\(\pm\) 0.008 & 0.067 \(\pm\) 0.003 & 0.076 \(\pm\) 0.004 & 0.061 \(\pm\) 0.001 \\ water-quality & **0.472**\(\pm\) 0.014 & 0.465 \(\pm\) 0.024 & 0.441 \(\pm\) 0.016 & 0.460 \(\pm\) 0.014 & 0.395 \(\pm\) 0.014 & 0.421 \(\pm\) 0.020 & 0.362 \(\pm\) 0.016 \\ flags & 0.723 \(\pm\) 0.043 & 0.708 \(\pm\) 0.031 & **0.724**\(\pm\) 0.046 & 0.709 \(\pm\) 0.039 & 0.708 \(\pm\) 0.026 & 0.699 \(\pm\) 0.015 & 0.698 \(\pm\) 0.031 \\ delicious & 0.182 \(\pm\) 0.005 & **0.228**\(\pm\) 0.005 & 0.216 \(\pm\) 0.003 & 0.177 \(\pm\) 0.004 & 0.220 \(\pm\) 0.002 & 0.116 \(\pm\) 0.004 & 0.175 \(\pm\) 0.033 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The experimental results (mean \(\pm\) standard) of all comparison methods in this paper in terms of Micro F1. \(\uparrow\) means that the larger the value, the better the performance is. The best results in each row are highlighted in bold face.