# Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework
## 5 Experimental Studies
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Index**} & \multicolumn{6}{c}{**Algorithms**} \\ \cline{3-8}  & & **ABC** & **NSABC** & **BABCWOA** & **BABCWOAWS** & **BABCGWO** & **BABCGWOWS** \\ \hline \multirow{3}{*}{LSVT} & worst & 0.112 & 0.121 & 0.103 & 0.104 & **0.056** & 0.064 \\  & mean \(\pm\) std & 0.102 \(\pm\) 0.01 & 0.106 \(\pm\) 0.01 & 0.075 \(\pm\) 0.02 & 0.074 \(\pm\) 0.02 & **0.044 \(\pm\) 0.01** & 0.046 \(\pm\) 0.01 \\  & best & 0.087 & 0.089 & 0.047 & 0.047 & **0.031** & **0.031** \\ \hline \multirow{3}{*}{Yale} & worst & 0.357 & 0.370 & 0.326 & 0.345 & **0.238** & 0.240 \\  & mean \(\pm\) std & 0.345 \(\pm\) 0.01 & 0.351 \(\pm\) 0.01 & 0.288 \(\pm\) 0.03 & 0.299 \(\pm\) 0.04 & **0.220 \(\pm\) 0.01** & **0.220 \(\pm\) 0.01** \\  & best & 0.327 & 0.327 & 0.241 & 0.243 & 0.210 & **0.207** \\ \hline \multirow{3}{*}{colon} & worst & 0.112 & 0.1 & 0.112 & 0.083 & **0.064** & **0.064** \\  & mean \(\pm\) std & 0.089 \(\pm\) 0.02 & 0.094 \(\pm\) 0.01 & 0.066 \(\pm\) 0.02 & 0.064 \(\pm\) 0.02 & **0.037 \(\pm\) 0.02** & 0.038 \(\pm\) 0.02 \\  & best & 0.0643 & 0.081 & 0.05 & 0.048 & **0.014** & **0.014** \\ \hline \multirow{3}{*}{SRBCT} & worst & 0.063 & 0.071 & 0.024 & 0.046 & **0.000** & **0.000** \\  & mean \(\pm\) std & 0.052 \(\pm\) 0.01 & 0.061 \(\pm\) 0.01 & 0.007 \(\pm\) 0.01 & 0.018 \(\pm\) 0.02 & **0.000** & **0.000** \\  & best & 0.022 & 0.047 & **0.000** & **0.000** & **0.000** & **0.000** \\ \hline \multirow{3}{*}{DBWorld} & worst & 0.121 & 0.110 & 0.093 & 0.074 & **0.033** & 0.048 \\  & mean \(\pm\) std & 0.103 \(\pm\) 0.01 & 0.092 \(\pm\) 0.01 & 0.048 \(\pm\) 0.02 & 0.046 \(\pm\) 0.02 & **0.025 \(\pm\) 0.01** & 0.031 \(\pm\) 0.01 \\  & best & 0.088 & 0.079 & 0.017 & 0.029 & **0.014** & **0.014** \\ \hline \multirow{3}{*}{Leukemia1} & worst & 0.068 & 0.086 & 0.043 & 0.071 & **0.014** & 0.029 \\  & mean \(\pm\) std & 0.047 \(\pm\) 0.02 & 0.065 \(\pm\) 0.01 & 0.023 \(\pm\) 0.02 & 0.043 \(\pm\) 0.02 & **0.001 \(\pm\) 0.01** & 0.004 \(\pm\) 0.01 \\  & best & 0.027 & 0.039 & **0.000** & 0.014 & **0.000** & **0.000** \\ \hline \multirow{3}{*}{DLBCL} & worst & 0.039 & 0.0518 & 0.041 & 0.041 & **0.025** & 0.038 \\  & mean \(\pm\) std & 0.029 \(\pm\) 0.01 & 0.027 \(\pm\) 0.02 & 0.016 \(\pm\) 0.01 & 0.025 \(\pm\) 0.01 & **0.010 \(\pm\) 0.01** & 0.012 \(\pm\) 0.01 \\  & best & 0.025 & **0.000** & **0.000** & **0.000** & **0.000** & **0.000** \\ \hline \multirow{3}{*}{ALLAML} & worst & 0.057 & 0.071 & 0.070 & 0.068 & **0.014** & **0.014** \\  & mean \(\pm\) std & 0.045 \(\pm\) 0.01 & 0.053 \(\pm\) 0.01 & 0.022 \(\pm\) 0.03 & 0.030 \(\pm\) 0.03 & **0.001 \(\pm\) 0.01** & 0.004 \(\pm\) 0.01 \\  & best & 0.029 & 0.029 & **0.000** & **0.000** & **0.000** & **0.000** \\ \hline \multirow{3}{*}{Pixraw10P} & worst & **0.000** & 0.010 & 0.010 & 0.010 & 0.010 & 0.010 \\  & mean \(\pm\) std & **0.000** & 0.001 \(\pm\) 0.00 & 0.002 \(\pm\) 0 & 0.003 \(\pm\) 0.0 & 0.003 \(\pm\) 0.01 & 0.005 \(\pm\) 0.01 \\  & best & **0.000** & **0.000** & **0.000** & **0.000** & **0.000** & **0.000** \\ \hline \multirow{3}{*}{Prostate} & worst & 0.089 & 0.089 & 0.089 & 0.078 & **0.060** & **0.060** \\  & mean \(\pm\) std & 0.074 \(\pm\) 0.01 & 0.084 \(\pm\) 0.00 & 0.069 \(\pm\) 0.02 & 0.062 \(\pm\) 0.01 & 0.044 \(\pm\) 0.01 & **0.039 \(\pm\) 0.01** \\  & best & 0.049 & 0.078 & 0.040 & 0.049 & 0.029 & **0.020** \\ \hline \multirow{3}{*}{Leukemia2} & worst & 0.043 & 0.070 & 0.043 & 0.039 & 0.027 & **0.000** \\  & mean \(\pm\) std & 0.032 \(\pm\) 0.01 & 0.040 \(\pm\) 0.01 & 0.014 \(\pm\) 0.01 & 0.013 \(\pm\) 0.01 & 0.004 \(\pm\) 0.01 & **0.000** \\  & best & 0.014 & 0.013 & **0.000** & **0.000** & **0.000** & **0.000** \\ \hline \multirow{3}{*}{GLL\_85} & worst & 0.081 & 0.079 & 0.074 & 0.082 & **0.061** & **0.061** \\  & mean \(\pm\) std & 0.062 \(\pm\) 0.01 & 0.064 \(\pm\) 0.01 & 0.050 \(\pm\) 0.01 & 0.058 \(\pm\) 0.02 & 0.045 \(\pm\) 0.01 & **0.040 \(\pm\) 0.01** \\ \cline{1-1}  & best & 0.046 & 0.047 & 0.033 & **0.025** & 0.035 & 0.0 To sum up, the proposed framework can effectively make the convergence speed faster, reduce the diversity, and find a better optimal solution.