## 5 Experimental Results
### Evaluation Results  
Having shown the main experimental setup and evaluation measurement, we now show our main evaluation results and their related discussions. **Evaluation of the case with no correlation:** Table 2 presents a comparison among MBHO (with no correlation) and BCS (with no correlation), evaluated across various datasets. Each dataset is represented with a row, and the columns detail different performance metrics and characteristics of the methods. These include the name of the dataset, the classification accuracy, the F1 score, the precision, the recall, the time taken for each algorithm to execute, and the number of features used for each one. By comparing these metrics, one can evaluate the performance of the two FS algorithms across different datasets and conditions, such as which method has a higher accuracy or F1 score, which runs faster, or which uses fewer features. This can assist in selecting the most suitable method for a specific task or dataset. It is important to note that the time is here formatted as _hours: minutes: seconds: milliseconds_, and the accuracy and F1 scores are likely proportions from 0 to 1. The features represent the number of variables used in the model to make its predictions, with a lower number indicating a less complex model, which can sometimes lead to better performance and interpretability. Moreover, according to the Wilcoxon test, these two algorithms are significantly different from each other due to the term of \(Acc\) at \(p<SL\). In this comparison between MBHO (with no correlation) and BCS (with no correlation) algorithms over various datasets see Table 2, MBHO generally outperformed BCS in terms of accuracy, precision, recall, and F1 score, but it also tended to use more features and take a longer time to run. For instance, with the Colon and Darwin datasets, MBHO (with no correlation) achieved higher accuracy, precision, recall, and F1 scores than BCS (with no correlation) despite using slightly more features and taking longer. With the Leukemia dataset, both methods performed equally in terms of accuracy, precision, recall, and F1 score, but MBHO (with no correlation) took longer and used fewer features. Similar trends were observed with the Leukemia-3c and MLL datasets. With the WDBC, Sobar, and Divorce datasets, both methods performed equally in terms of accuracy, precision, recall, and F1 score, but MBHO (with no correlation) took longer and used fewer features. For the SRBCT, Parkinson's, Sonar, and Urban datasets, MBHO achieved higher accuracy, precision,recall, and F1 scores, but it took longer and used more features. With the SpectTF and WPBC datasets, both methods had the same accuracy, precision, and recall, but MBHO (with no correlation) had a slightly higher F1 score, took longer, and used more features. In total, MBHO (with no correlation) yielded better accuracy, precision, and recall with eight datasets of the fourteen datasets and a better F1 score with eight datasets of all datasets, compared to BCS (with no correlation). While MBHO (with no correlation) may provide better performance, it may also be more computationally intensive and complex. Conversely, BCS (with no correlation) may be a more efficient choice in terms of runtime, but it may not perform as well as MBHO in terms of accuracy and F1 score. **Evaluation of Incorporating Proposed Correlation for Different Classifiers:** Table 3 presents a comparison between MBHO--correlation and BCS--correlation [37], based on Spearman's correlation function and a significance level of \(SL=0.05\). Here, the same classifier used in FS is the same one used for testing. The metrics used for this comparison are accuracy (_Acc_), precision, recall, F1 score (\(F1\)), the area under the curve (_AUC_), execution time, and the number of selected features. Under a KNN classifier with \(k=3\), for the testing, here are sample instances from the results for the \"Colon\" dataset.