# Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm
## 4 Conclusions  
In this paper, the WSA algorithm was applied to solve feature selection problems. Its performance was compared with several other algorithms, such as DE, GA, PSO, FPA, SOS, MPA and MRFO. Nine different datasets of varying complexity and domain were considered to analyze the performance of the algorithms. The algorithms were compared on varied criteria, such as fitness value, convergence, classification accuracy, number of features selected and computational time. Based on the exhaustive analysis, it can be concluded that: \begin{table}
\begin{tabular}{l l l l l l l l l} \hline
**Dataset** & **DE** & **GA (R)** & **GA (T)** & **PSO** & **FPA** & **SOS** & **MPA** & **MRFO** & **WSA** \\ \hline Heart & 4.4 & 4 & 4.6 & 4.6 & 5 & 3.2 & 4 & 4 & 4.6 \\  & (0.55) & (1.41) & (1.14) & (1.34) & (1.58) & (0.45) & (1.22) & (1.58) & (1.14) \\ Ionosphere & 15.4 & 9.8 & 9.4 & 10.4 & 14.4 & 3.6 & 4.2 & 17.6 & 5.2 \\  & (3.58) & (2.39) & (1.95) & (0.55) & (3.97) & (1.14) & (1.3) & (5.03) & (1.79) \\ German & 14.6 & 11.8 & 9 & 12.8 & 11 & 10.8 & 9 & 14.6 & 11.4 \\  & (2.3) & (2.68) & (0.71) & (1.64) & (2.35) & (2.59) & (2.35) & (3.21) & (3.05) \\ Breast & 4.4 & 3.6 & 4.8 & 5 & 4.4 & 4.2 & 4.4 & 4.4 & 3.4 \\  & (1.52) & (1.34) & (1.3) & (1.41) & (1.14) & (0.84) & (1.67) & (1.52) & (1.52) \\ Sonar & 33.2 & 14.8 & 14.4 & 16.6 & 21.8 & 7 & 10.8 & 31 & 16 \\  & (4.21) & (3.11) & (6.23) & (3.36) & (2.77) & (1.41) & (4.32) & (5.79) & (10.42) \\ Ovarian & 2443.2 & 1374.2 & 1325 & 1846.4 & 1924.4 & 5.4 & 6.4 & 2010.8 & 97.6 \\  & (324.56) & (39.86) & (24.9) & (30.06) & (23) & (3.58) & (1.67) & (91.38) & (56.42) \\ Australian & 4.8 & 3.4 & 3.4 & 3.6 & 4.2 & 5 & 3.8 & 9.4 & 3.8 \\  & (1.48) & (1.52) & (1.52) & (0.89) & (0.84) & (1.22) & (1.3) & (1.82) & (1.64) \\ Colon & 1061 & 543 & 499.6 & 836.8 & 936.8 & 2.2 & 1.6 & 970.4 & 3.8 \\  & (134.75) & (18.32) & (12.1) & (28.25) & (17.66) & (1.3) & (0.55) & (21.38) & (3.03) \\ Diabetes & 4.4 & 4.2 & 4.2 & 3.6 & 4.4 & 4.2 & 4.4 & 4.4 & 3.2 \\  & (0.89) & (0.84) & (0.84) & (1.52) & (1.14) & (0.45) & (1.14) & (0.55) & (1.1) \\ \hline Av. Rank (mean) & 7.61 & 3.94 & 4.11 & 5.78 & 6.67 & 2.67 & 3.22 & 7.39 & 3.61 \\ \hline Av. Rank (SD) & 5.78 & 5.44 & 4.28 & 4.67 & 4.61 & 2.11 & 4.56 & 7.39 & 6.17 \\ \hline Combined av. rank & 6.69 & 4.69 & 4.19 & 5.22 & 5.64 & 2.39 & 3.89 & 7.39 & 4.89 \\ \hline \end{tabular}
\end{table} 
Table 11: Comparison of accuracy of various algorithms on Heart dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**WSA-KNN** & **PSO-NN [59]** & **AGAFL [64]** & **RF [60]** & **Vote [61]** & **SVM [62]** & **DT [63]** \\ \hline
91.67 & 91.94 & 90 & 83.8 & 78.15 & 77.55 & 77.4 \\ \% Improvement & \(-0.3\%\) & 1.9\% & 9.4\% & 17.3\% & 18.2\% & 18.4\% \\ \hline \end{tabular}
\end{table} 
Table 10: Mean (and standard deviation) of features selected by each algorithm for each dataset on repeated trials. * WSA > MPA > GA (T) > MRFO > GA (R) > FPA > PSO > DE > SOS is the order of the algorithms under consideration that provide the best fitness value. In contrast, while comparing these algorithms based on mean best fitness and standard deviation, WSA > MPA > MRFO > FPA > GA > GA > GA > GA > GA > DE > PSO > SOS is the order of their performance. * The convergence of WSA and MPA is found to be superior to other algorithms. * WSA > GA (T) > GA (R) > DE > MPA > MRFO > PSO > FPA > SOS is the ranking order of the algorithms with respect to the highest classification accuracy. On the other hand, in terms of mean best classification accuracy and standard deviation, WSA > MPA > MRFO > GA (T) > GA (R) > FPA > DE > PSO > SOS is the order of the algorithms. * FPA and DE are noticed to be computationally faster than the other algorithms, and thus, based on the best computation time, the algorithms can be ranked as FPA > DE > WSA > PSO > MRFO > MPA > GA (T) > GA (R) > SOS. In terms of mean computational time and standard deviation, the ranking of these algorithms is FPA > DE > PSO > WSA > MRFO > MPA > GA (T) > GA (R) > SOS.