# Analyzing Physics-Inspired Metaheuristic Algorithms in Feature Selection with K-Nearest-Neighbor
## 4 Conclusions  
In this paper, six well-cited physics-inspired metaphor algorithms were employed for feature selection. Feature selection is one of the major challenges being faced in the field of data mining and machine learning. The objective of this research was to identify the most promising physics-inspired algorithms for the problem of feature selection. To accomplish this, six small- to large-sized datasets were used. The performance of EO was found to be superior on most of the datasets, and the metrics that were used for the comparative analysis were taken from the literature and included accuracy, fitness, the average number of features selected, and convergence analysis. The current physics-inspired metaphor algorithms, especially EO, SCA, and HGSO comprehensively outperformed other metaheuristics, as well as the ML-based solutions seen in recent literature. Based on our findings, we highly recommend using EO for the feature selection problem. Conceptualization, R.C. and K.K. ; Data curation, J.P., M.P. and M.J.; Formal analysis, J.P., M.P. and M.J.; Investigation, J.P., M.P. and M.J.; Methodology, R.C. and K.K. ; Software, R.C. and K.K. ; Validation, J.P., M.P. and M.J.; Visualization, J.P., M.P. and M.J.; Writing--original draft, J.P., M.P. and M.J.; Writing--review and editing, R.C. and K.K. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{1}{c}{**Method**} & \multicolumn{1}{c}{**Breast Cancer**} & \multicolumn{1}{c}{**\% Improvement \&**} & \multicolumn{1}{c}{**Ionosphere**} & \multicolumn{1}{c}{**\% Improvement**} & \multicolumn{1}{c}{**Sonar**} & \multicolumn{1}{c}{**\% Improvement**} \\ \hline EO & 0.995 & Best Solution & 0.986 & Best Solution & 0.976 & 2.46\% \\ SCA & 0.986 & 0.91\% & 0.971 & 1.54\% & 0.976 & 2.46\% \\ HGSO & 0.986 & 0.91\% & 0.986 & Best Solution & 0.951 & 5.15\% \\ GWO [37] & 0.970 & 2.58\% & 0.951 & 3.68\% & 0.970 & 3.09\% \\ MRG [37] & 0.605 & 64.64\% & 0.774 & 27.39\% & 0.547 & 82.82\% \\ WOA [37] & 0.973 & 2.26\% & 0.957 & 3.03\% & 0.976 & 2.46\% \\ SSA [37] & 0.982 & 1.32\% & 0.985 & 0.10\% & 1.000 & Best Solution \\ BOA [37] & 0.903 & 10.19\% & 0.901 & 9.43\% & 0.881 & 13.51\% \\ HHO [37] & 0.929 & 7.10\% & 0.929 & 6.14\% & 0.833 & 20.05\% \\ MPA [37] & 0.982 & 1.32\% & 0.985 & 0.10\% & 0.976 & 2.46\% \\ Naive Bayes [38] & 0.845 & 17.75\% & - & - & - & - \\ Logistic Regression [38] & 0.879 & 13.20\% & - & - & - & - \\ Random Forest [38] & 0.995 & Best Solution & - & - & - & - \\ SVM [38] & 0.620 & 60.48\% & - & - & - & - \\ K-NN [38] & 0.900 & 10.56\% & - & - & - & - \\ Decision Tree [38] & 0.880 & 13.07\% & - & - & - & - \\ SGD [38] & 0.903 & 10.19\% & - & - & - & - \\ PCA-Naive Bayes [38] & 0.975 & 2.05\% & - & - & - & - \\ PCA-Logistic Regression [38] & 0.975 & 2.05\% & - & - & - & - \\ PCA-Random Forest [38] & 0.962 & 3.43\% & - & - & - & - \\ PCA-SVM [38] & 0.942 & 5.63\% & - & - & - & - \\ PCA-K-NN [38] & 0.921 & 8.03\% & - & - & - & - \\ PCA-Decision Tree [38] & 0.905 & 9.94\% & - & - & - & - \\ PCA-SGD [38] & 0.916 & 8.62\% & - & - & - & - \\ \hline \hline \end{tabular}
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Table 7: Comparison of classification accuracy with literature results. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Method**} & \multicolumn{1}{c}{**Breast Cancer**} & \multicolumn{1}{c}{**\% Feature**} & \multicolumn{1}{c}{**\% Feature**} & \multicolumn{1}{c}{**\% Feature**} \\  & & **Reduction \&** & **Reduction** & **Sonar** & **Reduction** \\ \hline EO & 4.8 & 84\% & 5 & 85\% & 11.4 & 81\% \\ SCA & 4.8 & 84\% & 3.4 & 90\% & 8 & 87\% \\ HGSO & 3.8 & 87\% & 3.6 & 89\% & 7.8 & 87\% \\ GWO [37] & 7 & 77\% & 4 & 88\% & 11 & 82\% \\ MFO [37] & 6 & 80\% & 23 & 32\% & 31 & 48\% \\ WOA [37] & 8 & 73\% & 7 & 79\% & 26 & 57\% \\ SSA [37] & 11 & 63\% & 14 & 59\% & 16 & 73\% \\ BOA [37] & 12 & 60\% & 20 & 41\% & 26 & 57\% \\ HHO [37] & 12 & 60\% & 10 & 71\% & 20 & 67\% \\ MPA [37] & 12 & 60\% & 6 & 82\% & 8 & 87\% \\ \hline \hline \end{tabular}
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