## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Classifier** & **Accuracy** & **Precision** & **F1 score** & **EvalGBest** & **(nf, RMSE)** & **Time (ms)** \\ \hline \multirow{8}{*}{Breast Cancer Coimbra Data} & RF & **0.913043** & 0.926421 & 0.912714 & **0.332274** & (4, 0.294883) & 20,374 \\  & SVM & 0.652173 & 0.652173 & 0.652173 & 0.423882 & (6, 0.342954) & 1862 \\  & GST & 0.606895 & 0.611956 & 0.599542 & 0.360051 & (6, 0.257846) & 9458 \\  & LR & 0.826086 & 0.869565 & 0.819185 & 0.396104 & (3, 0.417028) & 7177 \\  & K-NN & **0.913043** & 0.913043 & 0.913043 & **0.332274** & (4, 0.294883) & 2644 \\  & NB & 0.782608 & 0.783946 & 0.781780 & 0.433022 & (3, 0.466252) & 1433 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: \(Cont.\)  
\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}{*}{Raisin Data} & RF & 0.855555 & 0.868158 & 0.854983 & 0.356472 & (2, 0.380058) & 26,458 \\  & SVM & **0.87222** & 0.880895 & 0.871965 & **0.339523** & (2, 0.357460) & 3743 \\  & GST & 0.866666 & 0.870097 & 0.866666 & 0.345289 & (2, 0.3656148) & 19,151 \\  & LR & 0.855555 & 0.862789 & 0.853541 & 0.356472 & (2, 0.380058) & 6513 \\  & K-NN & **0.87222** & 0.878514 & 0.872092 & **0.339523** & (2, 0.357460) & 9179 \\  & NB & 0.855555 & 0.860681 & 0.855466 & 0.356472 & (2, 0.380058) & 1441 \\ \hline \multirow{8}{*}{Turkish Music Emotion Data} & RF & 0.8625 & 0.872173 & 0.862413 & 0.532142 & (26, 0.536190) & 30,483 \\  & SVM & **0.925** & 0.926199 & 0.922436 & **0.45759** & (26, 0.433012) & 35,100 \\  & GBT & 0.8 & 0.816792 & 0.804053 & 0.568705 & (25, 0.591607) & 140,459 \\  & LR & 0.8875 & 0.891218 & 0.863630 & 0.530791 & (24, 0.547722) & 47,209 \\  & K-NN & 0.7625 & 0.790690 & 0.767970 & 0.630330 & (20, 0.707106) & 8163 \\  & NB & 0.8125 & 0.821741 & 0.814935 & 0.535791 & (25, 0.547722) & 1939 \\ \hline \multirow{8}{*}{Diabetes Risk Prediction Data} & RF & **0.990384** & 0.990532 & 0.990361 & **0.198543** & (8, 0.098058) & 20,801 \\  & SVM & 0.903846 & 0.916895 & 0.905011 & 0.295065 & (4, 0.310066) & 3723 \\  & GBT & **0.990384** & 0.990619 & 0.990406 & **0.198543** & (8, 0.098058) & 10,362 \\  & LR & 0.903846 & 0.916895 & 0.905011 & 0.295065 & (4, 0.310086) & 5936 \\  & K-NN & 0.980769 & 0.981684 & 0.980851 & 0.244631 & (9, 0.138675) & 6770 \\  & NB & 0.942307 & 0.943518 & 0.942553 & 0.289519 & (7, 0.240192) & 1393 \\ \hline \multirow{8}{*}{Rice Data} & RF & 0.913385 & 0.913333 & 0.91302 & 0.30460 & (2, 0.294322) & 54,663 \\  & SVM & **0.923884** & 0.924280 & 0.923666 & **0.920251** & (2, 0.275890) & 8067 \\  & GBT & 0.912073 & 0.912460 & 0.911803 & 0.300690 & (2, 0.289809) & 57,991 \\  & LR & 0.922572 & 0.922770 & 0.922397 & 0.92027 & (2, 0.278258) & 14,810 \\  & K-NN & 0.922572 & 0.928990 & 0.922366 & 0.920207 & (2, 0.278258) & 34,673 \\  & NB & 0.922572 & 0.922553 & 0.922484 & 0.920207 & (2, 0.278258) & 2 The classifiers which returned the best \(EvalGBest\) were SVM and RF, because each one returned the best value for three out of the nine datasets. The experimental results for the BPSO show that the algorithms which returned the best accuracy for most of the datasets (five out of nine), were RF and SVM. On the other hand, RF returned the best \(EvalGBest\) value for four out of the nine datasets. Finally, in the case of the BCSA, the SVM classifier returned the best accuracy for six out of the nine datasets, and the best \(EvalGBest\) value for five out of the nine datasets as well. Next, Tables 12-14 summarize the \(GBest\) values returned by the BGWO, BPSO, and BCSA, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Classifier** & **GBest Value** \\ \hline \multirow{8}{*}{K-NN} & RF & [1111100001111] \\  & SVM & [111000000001] \\ Smart & GBT & [1111000001111] \\ Grids Data & LR & [1110000000001] \\ \cline{1-1}  & K-NN & [111000000001] \\ \hline \hline \end{tabular}
\end{table}