# Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework
## 7 Conclusions  
There are often redundant and irrelevant features in high-dimensional datasets, so the FS method is used for data preprocessing. Aiming at the strong exploration ability of the ABC algorithm, this study proposes a framework that integrates the updating operators of the algorithm with strong exploitation abilities into the ABC algorithm to make the exploration and exploitation abilities balanced. Moreover, since the removal of the scout bee phase can weaken the exploration ability and save computational resources when processing high-dimensional datasets, the scout bee phase in the ABC algorithm is left out in our framework, and thus the BABCGWO algorithm and BABCWOA algorithm are pro  
Figure 5: The convergence curves of algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{2}{c}{**CSO**} & \multicolumn{2}{c}{**VSCCPSO**} & \multicolumn{2}{c}{**ALO\_GWO**} & \multicolumn{2}{c}{**ACABC**} \\ \cline{2-9}  & **BABCGWO** & **BABCWOA** & **BABCGWO** & **BABCWOA** & **BABCGWO** & **BABCWOA** & **BABCGWO** & **BABCWOA** \\ \hline \hline \multirow{2}{*}{LSVT} & 0(+) & 0.91(=) & 0(+) & 0.43(=) & 0(+) & 0.24(=) & 0(+) & 0(+) \\  & Yale & 0.19(=) & 0(-) & 0(+) & 0(+) & 0(+) & 0.03(+) & 0(+) & 0(+) \\  & colon & 0(+) & 0.06(=) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) \\  & SRBCT & 0.12(=) & 0.03(-) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) \\  & DBWorld & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) \\  & Leukemia1 & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) \\  & DLBCL & 0(+) & 0.03(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) \\  & ALLAML & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) \\  & Pixraw10P & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) \\  & Prostate & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) \\  & Leukemia2 & 0(+) & 0.14(=) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) \\  & GLI\_85 & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) & 0(+) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Wilcoxon rank sum test on the execution times of algorithms.  
posed to deal with the FS problem in high-dimensional datasets. The experimental results show that on 12 high-dimensional datasets, the BABCGWO algorithm and BABCWOA algorithm are significantly superior to other algorithms as regards dimensionality reduction, classification error rate and execution time. This shows that the proposed framework can balance the capabilities of exploration and exploitation, and effectively improve the overall performance in FS.  
However, the proposed method mainly focuses on the single-objective feature selection problem, where the main aim is to reduce the algorithm's classification error rate. In the future, we will investigate a multi-objective FS algorithm that simultaneously maximizes the classification performance and minimizes the number of selected features. Moreover, we would like to employ algorithms in different domains to verify their universality.  
Methodology, data curation, software, formal analysis, visualization, validation, writing--original draft preparation, Y.Z.; writing--review and editing, J.W., Y.Z. and S.H.; project administration, S.H. and X.L.; supervision, funding acquisition, S.H., X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (31870641), Natural Science Foundation of Fujian Province (2018J01612), and Forestry Science and Technology Projects in Fujian Province (Memorandums 26), China.  
Not applicable.  
The authors declare no conflict of interest.