# Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework
## 6 Further Analysis  
The comparisons in Section 5 show that the proposed BABCGWO algorithm and BABCWOA algorithm are more efficient than the ABC algorithm. To make a complete evaluation, we further verify the effectiveness of BABCGWO algorithm and BABCWOA algorithm by comparing them with four state-of-the-art FS algorithms on high-dimensional datasets, including the popular PSO variants named CSO [40] and VSCCPSO [41], the novel GWO variant ALO_GWO [42], and an ABC variant named ACABC [31]. In particular, CSO, VSCCPSO and ALO_GWO achieved excellent results in dealing with high-dimensional datasets. The parameters applied in CSO, VSCCPSO, ALO_GWO and ACABC here are the same as their own parameter settings. In this section, the classification error rate, the size of the feature subset, the computational time and the convergence curve of the six algorithms are investigated. The best results are shown in bold in the table. To further verify the improved effect of the two algorithms proposed in this paper, the [PERSON]'s rank sum test [43,44] with a significance level of 0.05 is applied to test the statistical significance between two different algorithms. The error rate, number of features and execution time of the two algorithms are tested by Wilcoxon rank sum test with another four FS algorithms. In the table of [PERSON] rank sum test, the symbol \"+\" indicates that the proposed algorithms are significantly better than the compared algorithm, the symbol \"=\" means that the performance of the two algorithms is similar, and the symbol \"\(-\)\" is opposite to \"+\", indicating that the proposed algorithms are significantly worse than other algorithms. Table 4 shows the worst, best, average, and standard deviation of the error rate for each algorithm. In terms of the maximum error rate and average error rate, the BABCGWO algorithm performed worse than any other algorithms in all datasets except Yale and Pixraw10P, and it was reduced by several percentage points on most datasets. The BABCWOA algorithm outperformed the four compared algorithms on half of the datasets, and achieved the best average error rate of all algorithms on the Pixraw10P dataset. In terms of minimum error rate, the BABCGWO algorithm's was lower than the other algorithms when applied all datasets except LVST, Yale and GLL_85. The BABCWOA algorithm also outperformed the four algorithms on more than half of the datasets. The standard deviation of the BABCGWO algorithm was \(\leq\)0.01 on most datasets and 0.02 on the colon dataset only, which is significantly superior to most other algorithms, indicating  
Figure 4: The execution time between different ABC-based methods. that the BABCGWO algorithm has better stability compared with the other algorithms. However, the standard deviation of the BABCOWA algorithm is mostly about 0.02, which is not much different from the four compared algorithms. To further illustrate whether the error rates of the algorithms proposed in this paper are significantly different from those of other algorithms, we use the Wilcoxon rank sum test. As can be seen from the Wilcoxon rank sum test results of the error rate in Table 5, compared with the ACABC and CSO algorithms, the error rates of the algorithms proposed in this paper were almost significantly lower than those of the other four algorithms when used on 12 datasets. Compared with the VSCPCSO algorithm, the BABCGWO algorithm was superior to the VSCPCSO algorithm when used on all datasets except LSVT, Yale and Pixraw10P. The BABCOWA algorithm was also significantly improved compared with the VSCPCSO algorithm when used on some datasets, and there was little difference