# Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework
## 5 Experimental Studies
### Experimental Results and Analysis
Specifically, BABCOWA's average error rate is at least 0.005 lower than the average error rate of the ABC algorithm, applied on the Prostate dataset. When used on the Yale dataset, the average error rate decreased the most, by nearly 0.06, and on the SRBCT and DBWorld datasets, the average error rate decreased by about 0.05. On other datasets, the average error rate also decreased by about 0.01 to 0.03. Moreover, the average error rate of BABCGWO was reduced more, especially on the Yale dataset, and BABCGWO decreased by 0.125 compared with the ABC algorithm. The average error rate of BABCGWO is at least 0.017 lower than that of the ABC algorithm when used on the GLI_85 dataset. There are seven datasets on which the average error rate decreased by more than 0.04. As you can see, the error rate of BABCGWOWS did not change much on most datasets compared to BABCGWO, and the results of comparison between BABCOWAWS and BABCWOA are similar. In terms of the worst error rate, the BABCWOA algorithm was reduced on half of the datasets, while the BABCGWO algorithm improved on all datasets except Pixraw10P, and the maximum error rate decreased the most on the Yale dataset (by 0.119). Both BABCWOA and BABCGWO decreased in terms of the error rate. It can be seen from the standard deviation of the algorithm that although the error rate of the BABCWOA algorithm improved on the whole, its error rate was not as stable as the ABC algorithm's on a few datasets, such as Yale and ALLAML. The stability of the BABCGWO algorithm is similar to that of the ABC algorithm. It can be concluded that the introduction of operators with strong exploitation ability into the proposed framework can indeed improve the ABC algorithm to a certain extent, and the scout bee phase is not active and has little effect on reducing the error rate of the algorithm. Figure 3: The convergence curves between different ABC-based methods. As per the results in Table 3, the number of features of the improved algorithm is more than that of the ABC algorithm. Although dimensionality reduction is one of the targets of FS, it is more important to achieve a lower error rate in many practical applications. Although the ABC algorithm has a small feature subset, it can be observed from the error rate in Table 2 that such a low number of selected features cannot achieve a low error rate. According to Figure 4, it is obvious that the calculation time will be reduced when the scout bee phase is removed. After the introduction of the GWO operator, the BABCGWO algorithm displayed little difference in time compared with the ABC or NSABC algorithms used on some datasets. In addition, BABCGWO was much faster than the ABC algorithm on colon, SRBCT, Leukemia1, DLBCL, ALLAML, Pixraw10P, Prostate, and Leukemia2 datasets. After the introduction of the WOA operator, the running time of the BABCWOA algorithm increased more or less on all datasets, except SRBCT and ALLAML, which may be because the running speed is also proportional to the feature number. It can be observed from Table 3 that the BABCWOA algorithm selects more features than other algorithms.