# Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm
## 3 Results and Discussion
### Performance of the Metaheuristic on the Real-World Datasets
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Dataset** & **DE** & **GA (R)** & **GA (T)** & **PSO** & **FPA** & **SOS** & **MPA** & **MRFO** & **WSA** \\ \hline Heart & 0.11 & 0.14 & 0.13 & 0.13 & 0.14 & 0.21 & 0.13 & 0.11 & 0.1 \\  & (0.04) & (0.04) & (0.03) & (0.05) & (0.06) & (0.05) & (0.03) & (0.04) & (0.01) \\ Ionosphere & 0.11 & 0.06 & 0.07 & 0.08 & 0.06 & 0.11 & 0.04 & 0.06 & 0.03 \\  & (0.04) & (0.02) & (0.02) & (0.03) & (0.04) & (0.01) & (0.01) & (0.03) & (0.01) \\ German & 0.22 & 0.21 & 0.22 & 0.21 & 0.21 & 0.24 & 0.2 & 0.21 & 0.2 \\  & (0.01) & (0.02) & (0.03) & (0.02) & (0.01) & (0.01) & (0.01) & (0.02) \\ Breast & 0.02 & 0.03 & 0.02 & 0.02 & 0.03 & 0.02 & 0.02 & 0.02 & 0.02 \\  & (0.01) & (0.01) & (0.01) & (0) & (0.01) & (0.01) & (0.01) & (0.01) & (0.01) \\ Sonar & 0.02 & 0.04 & 0.04 & 0.04 & 0.02 & 0.11 & 0.02 & 0.05 & 0.01 \\  & (0.02) & (0.03) & (0.04) & (0.02) & (0.01) & (0.09) & (0.02) & (0.04) & (0.01) \\ Ovarian & 0.02 & 0.02 & 0.03 & 0.02 & 0.02 & 0.05 & 0 & 0 & 0 \\  & (0.01) & (0.01) & (0.02) & (0.02) & (0.01) & (0.03) & (0) & (0) & (0) \\ Australian & 0.12 & 0.11 & 0.12 & 0.13 & 0.11 & 0.2 & 0.12 & 0.11 & 0.11 \\  & (0.02) & (0.01) & (0.03) & (0.02) & (0.02) & (0.05) & (0.01) & (0.01) & (0.01) \\ Colon & 0.14 & 0.05 & 0.05 & 0.17 & 0.07 & 0.12 & 0 & 0.02 & 0 \\  & (0.11) & (0.05) & (0.05) & (0.06) & (0.07) & (0.05) & (0) & (0.04) & (0) \\ Diabetes & 0.23 & 0.23 & 0.22 & 0.24 & 0.21 & 0.25 & 0.22 & 0.23 & 0.22 \\  & (0.02) & (0.02) & (0.01) & (0.01) & (0.02) & (0.04) & (0.02) & (0.02) & (0.02) \\ \hline Av. Rank (mean) & 5.61 & 5.00 & 5.44 & 6.33 & 5.11 & 8.56 & 3.00 & 4.33 & 1.61 \\ \hline Av. Rank (SD) & 5.89 & 5.44 & 5.56 & 5.44 & 5.00 & 6.44 & 2.67 & 5.22 & 3.33 \\ \hline Combined av. rank & 5.75 & 5.22 & 5.50 & 5.89 & 5.06 & 7.50 & 2.83 & 4.78 & 2.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean (and standard deviation) of best fitness value achieved by each algorithm for each dataset on repeated trials. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Dataset** & **DE** & **GA (R)** & **GA (T)** & **PSO** & **FPA** & **SOS** & **MPA** & **MRFO** & **WSA** \\ \hline Heart & 0.06908 & 0.08635 & 0.08481 & **0.05258** & 0.08635 & 0.15312 & 0.10131 & 0.08558 & 0.08481 \\ Ionosphere & 0.06245 & 0.04478 & 0.05951 & 0.03123 & 0.01591 & 0.10194 & 0.02946 & 0.02976 & **0.01503** \\ German & 0.20920 & 0.18320 & 0.18153 & 0.18940 & 0.19847 & 0.23187 & 0.19268 & 0.20217 & **0.16793** \\ Breast & 0.01157 & 0.01046 & **0.00667** & 0.01046 & 0.01869 & 0.01046 & 0.01157 & 0.01647 & 0.01157 \\ Sonar & 0.00433 & 0.00183 & 0.00267 & 0.00250 & 0.00283 & 0.00500 & **0.00100** & 0.00183 & **0.00100** \\ Ovarian & 0.00566 & 0.00324 & 0.00455 & 0.00451 & 0.00346 & 0.00486 & **0.00001** & 0.00011 & **0.00001** \\ Australian & 0.08966 & 0.09612 & **0.08106** & 0.10975 & 0.08823 & 0.16354 & 0.10975 & 0.08894 & 0.09683 \\ Colon & 0.00489 & 0.00263 & 0.00242 & 0.08677 & 0.00456 & 0.08721 & **0.00001** & **0.00001** & **0.00001** \\ Diabetes & 0.20559 & 0.20684 & 0.20559 & 0.22250 & 0.19515 & 0.21331 & 0.19265 & 0.19912 & **0.18618** \\ \hline Average rank & 6.39 & 4.78 & 4.11 & 5.39 & 5.17 & 8.11 & 4.06 & 4.39 & 2.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Best fitness function value achieved by each algorithm for each dataset. Figure 2: _Cont._  
Figure 2: Convergence of each algorithm for different datasets (**a**) Heart (**b**) Ionosphere (**c**) German (**d**) Breast (**e**) Sonar (**f**) Ovarian cancer (**g**) Australian (**h**) Colon (**i**) Diabetes.