# 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}{**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_ & 0.5830 \(\pm\) 0.02 & 0.7065 \(\pm\) 0.01 & 0.6994 \(\pm\) 0.01 & 0.6364 \(\pm\) 0.01 & 0.6109 \(\pm\) 0.02 & 0.6246 \(\pm\) 0.01 & 0.5964 \(\pm\) 0.01 & 0.6423 \\ _CMFS_ & 0.6753 \(\pm\) 0.00 & 0.7111 \(\pm\) 0.01 & 0.7014 \(\pm\) 0.01 & 0.6864 \(\pm\) 0.01 & 0.6732 \(\pm\) 0.01 & 0.6674 \(\pm\) 0.01 & 0.6430 \(\pm\) 0.01 & _0.6867_ \\ _LSF_ & 0.6761 \(\pm\) 0.01 & 0.7114 \(\pm\) 0.01 & 0.7032 \(\pm\) 0.01 & 0.68  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Approaches**}} & \multicolumn{6}{c}{**Data Sets**} & \multicolumn{1}{c}{**AVG. **} \\ \cline{2-10}  & \multicolumn{1}{c}{**Emotions**} & \multicolumn{1}{c}{**Yeast**} & \multicolumn{1}{c}{**Birds**} & \multicolumn{1}{c}{**Enron**} & \multicolumn{1}{c}{**Genbase**} & \multicolumn{1}{c}{**Business**} & \multicolumn{1}{c}{**Arts**} \\ \hline _MIFS_ & 0.2865 \(\pm\) 0.02 & 0.2006 \(\pm\) 0.01 & 0.0538 \(\pm\) 0.00 & 0.0544 \(\pm\) 0.00 & 0.0303 \(\pm\) 0.02 & 0.0270 \(\pm\) 0.00 & 0.0610 \(\pm\) 0.00 & _0.1019_ \\ _CMFS_ & 0.2600 \(\pm\) 0.01 & 0.2031 \(\pm\) 0.01 & 0.0535 \(\pm\) 0.00 & 0.0527 \(\pm\) 0.00 & 0.0303 \(\pm\) 0.02 & 0.0253 \(\pm\) 0.00 & 0.0568 \(\pm\) 0.00 & _0.0974_ \\ _LISF_ & 0.2697 \(\pm\) 0.01 & 0.1999 \(\pm\) 0.01 & 0.0532 \(\pm\) 0.00 & 0.0539 \(\pm\) 0.00 & 0.0303 \(\pm\) 0.02 & 0.0253 \(\pm\) 0.00 & 0.0568 \(\pm\) 0.00 & _0.0984_ \\ _ELC_ & **0.2517 \(\pm\) 0.02** & **0.1982 \(\pm\) 0.01** & **0.0518 \(\pm\) 0.00** & **0.0523 \(\pm\) 0.00** & **0.0049 \(\pm\) 0.00** & **0.0251 \(\pm\) 0.00** & **0.0567 \(\pm\) 0.00** & **0.0915** \\ \hline \multicolumn{1}{c}{\multirow{2}{*}{**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_ & 0.0430 \(\pm\) 0.00 & 0.0539 \(\pm\) 0.00 & 0.0373 \(\pm\) 0.00 & 0.0379 \(\pm\) 0.00 & 0.0353 \(\pm\) 0.00 & 0.0317 \(\pm\) 0.00 & 0.0557 \(\pm\) 0.00 & _0.0399_ \\ _CMFS_ & 0.0371 \(\pm\) 0.00 & 0.0536 \(\pm\) 0.00 & 0.0368 \(\pm\) 0.00 & 0.0350 \(\pm\) 0.00 & 0.0322 \(\pm\) 0.00 & 0.0280 \(\pm\) 0.00 & 0.0511 \(\pm\) 0.00 & _0.0394_ \\ _LISF_ & 0.0371 \(\pm\) 0.00 & 0.0536 \(\pm\) 0.00 & 0.0369 \(\pm\) 0.00 & 0.0349 \(\pm\) 0.00 & 0.0322 \(\pm\) 0.00 & 0.0280 \(\pm\) 0.00 & 0.0512 \(\pm\) 0.00 & 0.0394 \\ _ELC_ & **0.0368 \(\pm\) 0.00** & **0.0531 \(\pm\) 0.00** & **0.0366 \(\pm\) 0.00** & **0.0348 \(\pm\) 0.00** & **0.0319 \(\pm\) 0.00** & **0.0278 \(\pm\) 0.00** & **0.0508 \(\pm\) 0.00** & **0.0391** \\ \hline \multicolumn{1}{c}{\multirow{2}{*}{**(d)** Ranking loss (the lower the better).}} & \multicolumn{6}{c}{**Data Sets**} & \multicolumn{1}{c}{**AVG.