## 5 Experimental Results
### Evaluation Results
However, in most cases, it required more time to generate the final subset of features. KNN is non-probabilistic classifier; hence, _AUC_ scores were unavailable since they depend on probabilities. We highlight the best accuracy scores in bold. accuracy of 0.86, an F1 score of 0.86, and an AUC of 0.92. Also, MBHO--correlation took more time (54:23:664) and used more features (1732) than BCS (31:49:948 time and 1730 features). Under NB classifier with ten trees, here are sample instances from the results. For the \"Parkinson's\" dataset, MBHO--correlation achieved an accuracy of 0.78, an F1 score of 0.73, and an AUC of 0.86 in 44:860 with 10 features, while BCS achieved an accuracy of 0.79, an F1 score of 0.75, and an AUC of 0.85 in 29.326 with four features. For the \"Leukemia\" dataset, MBHO--correlation achieved an accuracy of 0.98 and an F1 score of 0.94, while BCS--correlation achieved an accuracy of 0.96 and an F1 score of 0.95. Also, MBHO--correlation took more time (54:23:664) and used more features (1732) than BCS (execution time of 31:49:948 and using 1730 features). 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 total, MBHO--correlation achieved a better performance compared to BCS--correlation over the datasets that were used. **Comparing the case with correlation to the case with no correlation in MBHO:** Table 4 presents a comparison between two versions of the MBHO algorithm, categorized according to their correlation status, detailing their performance across various datasets. Under the KNN classifier, the MBHO-correlation variant outperformed the MBHO--no correlation counterpart in terms of \(Acc\) with eight datasets, and it achieved the same performance with the other five datasets. Under the LR classifier, MBHO--correlation outperformed the MBHO--no correlation counterpart in terms of \(Acc\) only with one dataset and achieved the same performance with four datasets while performing more poorly with nine datasets. Under the XGBoost classifier, MBHO--correlation outperformed the MBHO--no correlation counterpart in terms of \(Acc\) with three datasets and achieved the same performance with four datasets while performing more poorly with seven datasets. Under the Naive Bayes classifier, MBHO--correlation outperformed the MBHO--no correlation counterpart in terms of \(Acc\) with two datasets and achieved the same performance with seven datasets while performing more poorly with four datasets. Furthermore, MBHO--correlation required more time to produce the final subset of features because of the correlation calculations. **Evaluation comparing MBHO with filter-based FS:** Table 5 presents the performance comparison between MBHO with Spearman's correlation function and three baseline filter FS approaches, MIM [38], JMI [39], and mRMR [40], under four different classifiers, where the classifier used in FS was the same classifier used for testing. The results show that, under the KNN classifier, MBHO with Spearman's correlation function achieved the highest sum of ranks and mean of ranks, and it was ranked first fourteen times, implying its overall superiority across the evaluated datasets. And here, the null hypothesis was rejected. Under the LR classifier, [PERSON] with Spearman's correlation function was ranked the first algorithm and achieved the best performance among all the given algorithms. It provided the highest mean and sum of ranks, according to the Friedman test, and the null hypothesis was rejected, which means that there was a significant difference among these approaches, corresponding to the given observations (datasets). Moreover, under the XGBoost classifier, MBHO with Spearman's correlation function was ranked third after MIM [38] and JMI [39], while achieving better performance than mRMR [40], and the null hypothesis was rejected.