# An Effective Multi-Label Feature Selection Model Towards Eliminating Noisy Features
## 5 Experimental Evaluation
### Example 1: Classification Performance
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Data Sets**} & \multicolumn{1}{c}{**AVG.**} \\ \cline{2-10}
**Approaches** & \multicolumn{1}{c}{**Emotions**} & \multicolumn{1}{c}{**Yeast**} & \multicolumn{1}{c}{**Birds**} & \multicolumn{1}{c}{**Eron**} & \multicolumn{1}{c}{**Genbase**} & \multicolumn{1}{c}{**Business**} & \multicolumn{1}{c}{**Arts**} & \multicolumn{1}{c}{_AVG._} \\ \hline _MIFS_ & 0.6667 \(\pm\) 0.04 & 0.7520 \(\pm\) 0.02 & 0.9398 \(\pm\) 0.02 & 0.6139 \(\pm\) 0.03 & 0.7361 \(\pm\) 0.15 & 0.8812 \(\pm\) 0.00 & 0.5108 \(\pm\) 0.01 & _0.6506_ \\ _CMFS_ & 0.7221 \(\pm\) 0.02 & 0.7464 \(\pm\) 0.01 & 0.4116 \(\pm\) 0.03 & 0.6206 \(\pm\) 0.01 & 0.7342 \(\pm\) 0.15 & 0.892 \(\pm\) 0.00 & 0.5611 \(\pm\) 0.00 & _0.6697_ \\ _LLSF_ & 0.7016 \(\pm\) 0.02 & 0.7532 \(\pm\) 0.01 & 0.4231 \(\pm\) 0.07 & 0.6197 \(\pm\) 0.04 & 0.7352 \(\pm\) 0.15 & 0.8924 \(\pm\) 0.00 & 0.5615 \(\pm\) 0.01 & _0.6695_ \\ _ELC_ & **0.7306 \(\pm\) 0.02** & **0.7564 \(\pm\) 0.01** & **0.4671 \(\pm\) 0.08** & **0.6347 \(\pm\) 0.01** & **0.9868 \(\pm\) 0.00** & **0.8931 \(\pm\) 0.00** & **0.5646 \(\pm\) 0.00** & _0.7190_ \\ \hline _Education_ & \multicolumn{1}{c}{**Reaction**} & \multicolumn{1}{c}{**Health**} & \multicolumn{1}{c}{**Computers**} & \multicolumn{1}{c}{**Science**} & \multicolumn{1}{c}{**Reference**} & \multicolumn{1}{c}{**Society**} \\ \hline _MIFS_ & 0.5129 \(\pm\) 0.01 & 0.5836 \(\pm\) 0.01 & 0.7391 \(\pm\) 0.02 & 0.6629 \(\pm\) 0.01 & 0.4929 \(\pm\) 0.02 & 0.6267 \(\pm\) 0.01 & 0.5899 \(\pm\) 0.01 & _0.5963_ \\ _CMFS_ & 0.6164 \(\pm\) 0.01 & 0.5883 \(\pm\) 0.01 & 0.7437 \(\pm\) 0.01 & 0.6931 \(\pm\) 0.00 & 0.5477 \(\pm\) 0.01 & 0.6718 \(\pm\) 0.01 & 0.6463 \(\pm\) 0.01 & _0.6439_ \\ _LLSF_ & 0.6163 \(\pm\) 0.01 & 0.5880 \(\pm\) 0.01 & 0.7435 \(\pm\) 0.01 & 0.6932 \(\pm\) 0.00 & 0.5478 \(\pm\) 0.01 & 0.6720 \(\pm\) 0.00 & 0.6460 \(\pm\) 0.01 & _0.6438_ \\ _ELC_ & **0.6213 \(\pm\) 0.00** & **0.5952 \(\pm\) 0.00** & **0.7469 \(\pm\) 0.01** & **0.6962 \(\pm\) 0.00** & **0.5565 \(\pm\) 0.00** & **0.6742 \(\pm\) 0.00** & **0.6500 \(\pm\) 0.00** & _0.6486_ \\ \hline \hline \end{tabular} \begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Data Sets**} & \multicolumn{1}{c}{**AVG.**} \\ \cline{2-10}
**Approaches** & \multicolumn{1}{c}{**Emotions**} & \multicolumn{1}{c}{**Yeast**} & \multicolumn{1}{c}{**Birds**} & \multicolumn{1}{c}{**Eron**} & \multicolumn{1}{c}{**Genbase**} & \multicolumn{1}{c}{**Business**} & \multicolumn{1}{c}{**Arts**} & \multicolumn{1}{c}{_AVG._} \\ \hline _MIFS_ & 0.6601 \(\pm\) 0.53 & 0.6554 \(\pm\) 0.03 & 0.6497 \(\pm\) 0.02 & 0.5968 \(\pm\) 0.03 & 0.7886 \(\pm\) 0.10 & 0.6371 \(\pm\) 0.01 & 0.6100 \(\pm\) 0.01 & _0.6568_ \\ _CMFS_ & 0.7307 \(\pm\) 0.03 & 0.6473+0.03 & 0.6403 \(\pm\) 0.04 & 0.6194 \(\pm\) 0.01 & 0.7883 \(\pm\) 0.10 & 0.6821 \(\pm\) 0.01 & 0.6606 \(\pm\) 0.01 & _0.6812_ \\ _LSF_ & 0.7069 \(\pm\) 0.02 & 0.6601 \(\pm\) 0.02 & 0.6793 \(\pm\) 0.05 & 0.6092 \(\pm\) 0.03 & 0.7887 \(\pm\) 0.10 & 0.6824 \(\pm\) 0.01 & 0.6608 \(\pm\) 0.01 & _0.6839_ \\ _ELC_ & **0.7513 \(\pm\) 0.02** & **0.6706 \(\pm\) 0.02** & **0.7018 \(\pm\) 0.06** & **0.6385 \(\pm\) 0.01** & **0.9663 \(\pm\) 0.00** & **0.6834 \(\pm\) 0.00** & **0.6659 \(\pm\) 0.00** & **0.7254** \\ \hline \hline \end{tabular}