# Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework
## 5 Experimental Studies
### Experimental Results and Analysis  
To test the performance of our proposed framework, the diversity, convergence curves, classification error rate, size of feature subset and computing time of algorithms are investigated in this subsection. The best results are shown in bold in the tables. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Features** & **Samples** & **Classes** \\ \hline LSVT & 310 & 126 & 2 \\ Yale & 1024 & 165 & 15 \\ colon & 2000 & 62 & 2 \\ SRBCT & 2308 & 83 & 4 \\ DBWorld & 4702 & 64 & 2 \\ Leukemia1 & 5327 & 72 & 3 \\ DLBCL & 5469 & 77 & 2 \\ ALLAML & 7129 & 72 & 2 \\ Pixraw10P & 10,000 & 100 & 10 \\ Prostate & 10,509 & 102 & 2 \\ Leukemia2 & 11,225 & 72 & 3 \\ GLI\_85 & 22,283 & 85 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description for datasets. Figure 2 shows the diversity curves of six algorithms on 12 datasets. We can see that the diversity of ABC is obviously higher than that of other algorithms on all datasets, except DBWorld and Pixraw10P. According to the search process of the ABC algorithm, the exploration performance of the ABC algorithm is stronger, so its diversity is higher. In the early stage, high diversity can avoid trapping in the local optimization, but after a limited number of cycles, we need to find the optimal solution. The diversity of NSABC decreases a lot, which weakens the exploration ability of the ABC algorithm. After introducing the GWO and WOA operators into the framework, the diversity of these algorithms decreases faster than that of the NSABC algorithm in most datasets. The lower the diversity, the weaker the exploration ability of the algorithm and the stronger the exploitation ability of the algorithm. This shows that the introduction of the GWO and WOA operators strengthens the exploitation ability of the algorithm effectively. In addition, Figure 2 shows that the diversity curves of the BABCGWOWS algorithm and BABCGWO algorithm are similar, and the diversity curves of the BABCOWAWS algorithm and BABCWOA algorithm are not very different. It can be seen that scout bees have little effect on the diversity of the algorithms in this framework. The convergence curves of the algorithms are plotted in Figure 3. This shows the decline of the error rate. Each curve is plotted by averaging the error rate obtained at each generation of the 10 runs. The convergence results of NSABC and ABC are similar on LSVT, Yale, colon, DBWorld, DLBCL, Pixraw10P and GLI_85 datasets, and the convergence results of the NSABC algorithm are slightly higher than that of the ABC algorithm on other datasets. Obviously, compared with the ABC algorithm, the BABCGWO and BABCWOA algorithms converge faster with a good-quality solution. On most datasets, the error rates of BABCGWO and BABCWOA are similar to or lower than those of BABCGWOWS and BABCWOAWS, respectively. It can be concluded that the BABCGWO and BABCWOA algorithms perform better than ABC in terms of both convergence speed and solution quality. Table 2 shows the worst, the best, the mean and the standard deviation of the error rate results of each algorithm. The ultimate goal of FS is to improve generalization performance, which means achieving a lower error rate when used on unforeseeable data. A lower error rate indicates that the algorithm can find a better feature subset. Since almost all the SI-based algorithms are stochastic in nature, they may produce different results in each  
Figure 2: The diversity curves between different ABC-based methods. run. Therefore, standard deviation is conducted to measure the variations in the results. The smaller the standard deviation is, the more stable the algorithm is. From Table 2, we can see that the error rate of the NSABC algorithm is slightly higher than that of the ABC algorithm used on most datasets, but the increase is not more than 0.01. The error rate was improved on all datasets except Pixraw10P after the introduction of the operator with strong exploitation ability in the onlooker bee phase.