# 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 l l l l l l l l l} \hline
**Dataset** & **DE** & **GA (R)** & **GA (T)** & **PSO** & **FPA** & **SOS** & **MPA** & **MRFO** & **WSA** \\ \hline Heart & 0.89 & 0.87 & 0.87 & 0.87 & 0.86 & 0.79 & 0.88 & 0.89 & 0.9 \\  & (0.04) & (0.04) & (0.03) & (0.05) & (0.06) & (0.05) & (0.03) & (0.04) & (0.01) \\ Ionosphere & 0.9 & 0.94 & 0.95 & 0.92 & 0.91 & 0.9 & 0.96 & 0.94 & 0.97 \\  & (0.04) & (0.04) & (0.02) & (0.03) & (0.03) & (0.01) & (0.01) & (0.03) & (0.01) \\ German & 0.79 & 0.8 & 0.79 & 0.79 & 0.79 & 0.76 & 0.8 & 0.79 & 0.81 \\  & (0.01) & (0.02) & (0.03) & (0.02) & (0.01) & (0.01) & (0.01) & (0.01) & (0.02) \\ Breast & 0.99 & 0.98 & 0.99 & 0.99 & 0.98 & 0.98 & 0.99 & 0.98 & 0.99 \\  & (0.01) & (0.01) & (0.01) & (0) & (0.01) & (0.01) & (0.01) & (0.01) & (0.01) \\ Sonar & 0.98 & 0.98 & 0.97 & 0.97 & 0.94 & 0.89 & 0.99 & 0.96 & 0.99 \\  & (0.02) & (0.01) & (0.03) & (0.02) & (0.02) & (0.09) & (0.02) & (0.04) & (0.01) \\ Ovarian & 0.99 & 0.98 & 0.99 & 0.99 & 0.99 & 0.95 & 1 & 1 & 1 \\  & (0.01) & (0.01) & (0.01) & (0.02) & (0.01) & (0.03) & (0) & (0) & (0) \\ Australian & 0.89 & 0.89 & 0.88 & 0.87 & 0.89 & 0.8 & 0.88 & 0.89 & 0.89 \\  & (0.02) & (0.01) & (0.03) & (0.02) & (0.02) & (0.05) & (0.01) & (0.01) & (0.01) \\ Colon & 0.87 & 0.95 & 0.95 & 0.83 & 0.93 & 0.88 & 1 (0) & 0.98 & 1 \\  & (0.11) & (0.05) & (0.05) & (0.06) & (0.07) & (0.05) & 1 (0) & (0.04) & (0) \\ Diabetes & 0.77 & 0.77 & 0.78 & 0.76 & 0.79 & 0.75 & 0.79 & 0.77 & 0.79 \\  & (0.02) & (0.02) & (0.01) & (0.01) & (0.02) & (0.04) & (0.02) & (0.02) & (0.02) \\ \hline Av. Rank (mean) & 5.33 & 5.44 & 5.00 & 6.17 & 5.61 & 8.44 & 3.11 & 4.28 & 1.61 \\ \hline Av. Rank (SD) & 5.94 & 5.17 & 5.06 & 5.61 & 5.56 & 6.67 & 2.83 & 4.89 & 3.28 \\ \hline Combined av. rank & 5.64 & 5.31 & 5.03 & 5.89 & 5.58 & 7.56 & 2.97 & 4.58 & 2.44 \\ \hline \end{tabular}
\end{table} 
Table 6: Mean (and standard deviation) of best classification accuracy achieved by each algorithm for each dataset in repeated trials. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline
**Dataset** & **DE** & **GA (R)** & **GA (T)** & **PSO** & **FPA** & **SOS** & **MPA** & **MRFO** & **WSA** \\ \hline Heart & 37.3069 & 58.4510 & 59.3564 & 36.9822 & 37.2579 & 125.6186 & 71.1589 & 66.0726 & **35.9040** \\ Ionosphere & **4.8874** & 14.3396 & 14.8998 & 9.2014 & 4.9101 & 18.4415 & 9.8246 & 8.2109 & 5.4902 \\ German & **41.4371** & 126.0874 & 150.9288 & 94.9779 & 46.2631 & 171.6129 & 87.2630 & 75.1673 & 45.2955 \\ Breast & 42.1620 & 64.1089 & 65.3039 & 42.1937 & **37.8479** & 137.6404 & 74.1079 & 62.2471 & 45.1631 \\ Sonar & 32.8366 & 56.9434 & 54.5112 & 32.6148 & **32.5553** & 129.3449 & 65.7633 & 56.0746 & 34.6354 \\ Ovarian & 126.6548 & 137.8998 & 137.5729 & 122.3213 & 103.2913 & 155.3433 & 85.1104 & **83.8790** & 169.3444 \\ Australian & 38.4591 & 61.5668 & 62.1789 & 38.4547 & 38.7062 & 139.0013 & 77.3190 & 53.8778 & **37.9527** \\ Colon & 38.3924 & 56.6966 & 56.6441 & 47.9606 & **37.6771** & 109.1697 & 67.4664 & 53.6178 & 42.1641 \\ Diabetes & 86.3755 & 133.5861 & 130.1496 & 81.9149 & 78.9590 & 324.7326 & 73.3953 & 62.7816 & **39.9376** \\ \hline Average rank & 3.00 & 6.67 & 6.67 & 3.67 & 2.44 & 8.89 & 6.22 & 4.33 & 3.11 \\ \hline \end{tabular}
\end{table} 
Table 7: Best computational time achieved by each algorithm for each dataset. The primary task of the feature selection process is to eliminate redundant features from further analyses. The lowest numbers of features selected by the algorithms for each dataset are depicted in Table 9. For large-feature datasets, such as Ovarian Cancer, the classical algorithms, such as DE and GA, reduce the features by approximately half and two-thirds, respectively. However, algorithms such as SOS, MPA and WSA are able to achieve 99% or more reduction in features. When this is taken into account in conjunction with the fact that these algorithms clock a 100% classification rate on this dataset, this outcome appears even more remarkable.