## 7 Conclusions and Future Directions  
In this paper, various FS approaches were developed using a recently introduced swarm-based optimizer named HHO. The proposed methods integrate the HHO algorithm with V-shaped time-varying binarization schemes to enable HHO to work in a binary search space. Various well-known datasets from the UCI data repository were utilized for evaluating the introduced approaches, and the results of the best approach BHHO\({}_{TV4}\) were compared with those obtained from several meta-heuristic-based FS approaches such as BGSA, BPSO, BBA, BSSA, and BWOA. It is clear from the obtained results that the efficiency of HHO in the FS domain is highly influenced by the binarization scheme used. The proposed BHHO\({}_{TV4}\) can often overtake other FS approaches presented in previous studies. In future work, we will study the effect of using S-shaped time-varying binarization schemes on the performance of HHO in the FS problem.  
Conceptualization, T.T. and M.M.; Methodology, H.C., T.T., H.T. and M.M.; implementation and experimental work, H.C., T.T., H.T. and M.M.; Validation, H.C., T.T., H.T., M.M. and A.S.; Writing original draft preparation, H.C., T.T. and H.T.; Writing review and editing, M.M. and A.S.; Proofreading, A.S.; Supervision, M.M.; funding acquisition, H.T. All authors have read and agreed to the published version of the manuscript.  
Taif University Researchers Supporting Project number (TURSP-2020/125), Taif University, Taif, Saudi Arabia.  
Not applicable.  
The authors would like to acknowledge Taif University Researchers Supporting Project Number (TURSP-2020/125), Taif University, Taif, Saudi Arabia.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Dataset** & **BHHOTV4** & **BSSA\_S3,CP [(58)]** & **WOA-CM [(59)]** & **BGQA\_ETPD\_Tour [(60)]** & **HGSA [(61)]** & **BOOA-M [(62)]** & **BDA-TV4 [(38)]** & **BGWOPOSO [(12)]** & **S-BOA [(63)]** \\ \hline BreastCancer & 0.978 & 0.977 & 0.968 & **0.980** & 0.974 & 0.974 & 0.977 & **0.980** & 0.9866 \\ BreastEW & **0.979** & 0.948 & 0.971 & 0.947 & 0.971 & 0.970 & 0.974 & 0.970 & 0.9709 \\ CongressEW & **1.000** & 0.963 & 0.792 & 0.964 & 0.966 & 0.995 & 0.980 & 0.9593 \\ Exactly & 1.000 & 0.980 & 0.956 & 0.999 & **1.000** & **1.000** & 0.929 & **1.000** & 0.9724 \\ Exactly2 & 0.789 & 0.758 & **1.000** & 0.780 & 0.770 & 0.735 & 0.726 & 0.760 & 0.7596 \\ HeartEW & **0.910** & 0.861 & 0.742 & 0.833 & 0.856 & 0.836 & 0.886 & 0.850 & 0.8237 \\ IonosphereEW & **0.973** & 0.918 & 0.919 & 0.899 & 0.954 & 0.946 & 0.925 & 0.950 & 0.967 \\ KrxysEW & 0.979 & 0.964 & 0.866 & 0.968 & 0.978 & 0.974 & 0.971 & **0.980** & 0.966 \\ Lymphography & **0.981** & 0.890 & 0.807 & 0.868 & 0.892 & 0.912 & 0.895 & 0.920 & 0.8676 \\ M-of-n & **1.000** & 0.992 & 0.926 & **1.000** & **1.000** & **1.000** & 0.973 & **1.000** & 0.972 \\ pendingEW & **1.000** & 0.878 & 0.972 & 0.927 & 0.956 & 0.934 & 0.807 & 0.960 & 0.8775 \\ SonarEW & 0.975 & 0.937 & 0.852 & 0.912 & 0.958 & 0.915 & **0.995** & 0.960 & 0.9362 \\ SpectEW & 0.909 & 0.836 & **0.991** & 0.826 & 0.919 & 0.826 & 0.877 & 0.880 & 0.8463 \\ Tic-tac-toc & **0.833** & 0.821 & 0.785 & 0.808 & 0.788 & 0.791 & 0.822 & 0.810 & 0.7983 \\ Vote & **0.987** & 0.951 & 0.939 & 0.966 & 0.973 & 0.963 & 0.962 & 0.970 & 0.9653 \\ WaveformEW & 0.797 & 0.734 & 0.753 & 0.737 & **0.815** & 0.751 & 0.749 & 0.800 & 0.7429 \\ WineLW & **1.000** & 0.993 & 0.959 & 0.989 & 0.989 & 0.999 & **1.000** & 0.9843 \\ Zoo & **1.000** & **1.000** & 0.980 & 0.993 & 0.932 & 0.958 & 0.983 & **1.000** & 0.9775 \\ \hline Rank (F-test) & **1.78** & 6.00 & 6.78 & 5.92 & 4.28 & 5.50 & 4.86 & 3.03 & 6.86 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison of the proposed \(BHHO_{TV4}\) and other approaches from previous works in terms of accuracy rates.