## 4 Results
### BHOA Experimental Results  
Table 3 presents the configuration parameters of the BHOA used in experiments. The value of \(D\) is different for each dataset, and is equal to the number of features from Table 2. The objective function \(OF\) is represented by Formula (7). Table 4 presents the BHOA experimental results. The columns are: (1) Dataset--the preprocessed data used in experiments, (2) Classifier--one of the classifiers from the possible set of classifiers [RF, SVM, GBT, LR, K-NN, NB], (3) Accuracy--the accuracy for testing data, (4) Precision--the precision for testing data, (5) F1 score--the F1 score for testing data, (6) EvalGBest--the evaluated value of the global best individual after the running of all iterations, (7) (nf, RMSE)--the number of selected features and the RMSE computed using the testing data and the applied classifier, and (8) Time (ms)--the running time of the algorithm for \(I\) iterations. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Classifier** & **Accuracy** & **Precision** & **F1 Score** & **EvalGBest** & **(nf, RMSE)** & **Time (ms)** \\ \hline \multirow{8}{*}{Smart Grids Data} & RF & **0.912** & 0.912548 & 0.910972 & 0.409985 & (9, 0.296647) & 998,67e \\  & SVM & 0.694 & 0.668913 & 0.666302 & 0.46546 & (2, 0.553172) & 361,459 \\ \cline{1-1}  & GBT & 0.899 & 0.898627 & 0.898736 & 0.425853 & (9, 0.317804) & 1,411,226 \\ \cline{1-1}  & LR & 0.6935 & 0.685425 & 0.667392 & 0.456884 & (2, 0.553624) & 127,434 \\ \cline{1-1}  & K-NN & 0.897 & 0.897719 & 0.895522 & **0.407368** & (8, 0.320936) & 1,509,808 \\ \cline{1-1}  & NB & 0.7005 & 0.697274 & 0.670953 & 0.452116 & (2, 0.547265) & 19,477 \\ \hline \multirow{8}{*}{Raisin Data} & RF & 0.85 & 0.858348 & 0.849698 & 0.361902 & (2, 0.387298) & 109,878 \\ \cline{1-1}  & SVM & **0.87222** & 0.880895 & 0.871965 & **0.339523** & (2, 0.357460) & 17,181 \\ \cline{1-1}  & GBT & 0.866666 & 0.870097 & 0.866666 & 0.345289 & (2, 0.365148) & 89,852 \\ \cline{1-1}  & LR & 0.855355 & 0.865277 & 0.855180 & 0.356472 & (2, 0.380058) & 26,454 \\ \cline{1-1}  & K-NN & 0.85 & 0.860997 & 0.849513 & 0.361902 & (2, 0.387298) & 38,053 \\ \cline{1-1}  & NB & 0.85 & 0.864039 & 0.849289 & 0.361902 & (2, 0.387298) & 5984 \\ \hline \multirow{8}{*}{Turkish Music Emotion Data} & RF & 0.9 & 0.905083 & 0.900721 & 0.545 & (34, 0.5) & 125,597 \\ \cline{1-1}  & SVM & **0.925** & 0.923872 & 0.922807 & **0.485473** & (39, 0.387298) & 169,451 \\ \cline{1-1}  & GBT & 0.775 & 0.811160 & 0.786773 & 0.589279 & (26, 0.612372) & 797,135 \\ \cline{1-1}  & LR & **0.925** & 0.929673 & 0.923135 & 0.530756 & (35, 0.47431) & 308,861 \\ \cline{1-1}  & K-NN & 0.825 & 0.832499 & 0.823849 & 0.685330 & (31, 0.707106) & 40,878 \\ \cline{1-1}  & NB & 0.85 & 0.857903 & 0.851368 & 0.514260 & (26, 0.512347) & 11,822 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: BHOA Experimental Results Summary. \begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration Parameter** & **Value** \\ \hline \(I\) (number of iterations) & 50 \\ \(N\) (number of horses) & 30 \\ \(M\) (horses reorganization frequency) & 5 \\ \(DSP\) (dominant stallions percent) & 10\% \\ \(HDR\) (horses distribution rate) & 10\% \\ \(HMP\) (horse memory pool) & 3 \\ \(SSP\) (single stallions percent) & 10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: BHOA Configuration Parameters. As can be seen in the table, the classifier that returned the best accuracy results was SVM, as it returned the best accuracy results for six out of the nine datasets used in the experiments. Another observation is that all classifiers returned the best accuracy result for the Chronic Kidney Disease Data. The classifier which returned the best \(EvalGBest\) value in most cases was SVM as it returned the best value for five datasets. Table 5 presents the summary of the BHOA \(GBest\) values. The selected features are marked with 1, while the features which are not selected are marked with 0.