## 5 Numerical Experiment
**} & \multicolumn{4}{c}{**Classifier Quality, \%**} & \multicolumn{4}{c}{**Classifier Performance, ms**} \\ \cline{3-10}  & & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Predictions**} \\ \cline{5-10}  & & & & & & **Mean** & **SD** & **Mean** & **SD** \\ \hline \multirow{5}{*}{1} & RF & 99.90 & 99.86 & 99.92 & 99.88 & 128.78 & 1.92 & 11.86 & 0.18 \\  & SVM & 100.00 & 100.00 & 100.00 & 100.00 & 12.17 & 1.48 & 8.92 & 0.87 \\  & KNN & 100.00 & 100.00 & 100.00 & 100.00 & 0.79 & 0.01 & 19.67 & 2.71 \\  & ELM & 100.00 & 100.00 & 99.99 & 99.99 & 2.60 & 0.58 & **1.84** & **0.11** \\ \hline \multirow{5}{*}{2} & RF & 98.06 & 96.77 & 98.31 & 97.45 & 489.33 & 11.52 & 38.69 & 0.47 \\  & SVM & 99.80 & 99.59 & 99.83 & 99.70 & 38.87 & 2.85 & 48.10 & 2.99 \\  & KNN & 99.79 & 99.54 & 99.81 & 99.67 & **1.09** & **0.02** & 32.60 & 1.39 \\  & **ELM** & **99.82** & **99.72** & **99.82** & **99.77** & 10.06 & 1.03 & **6.93** & **0.27** \\ \hline \multirow{5}{*}{3} & RF & 99.51 & 98.59 & 99.71 & 99.04 & 330.80 & 11.42 & 27.99 & 0.56 \\  & **SVM** & **99.98** & **99.99** & **99.99** & **99.99** & 18.71 & 2.00 & 14.79 & 1.07 \\  & KNN & 99.96 & 99.97 & 99.98 & 99.97 & **1.02** & **0.03** & 26.97 & 1.21 \\  & ELM & 99.93 & 99.95 & 99.96 & 99.95 & 10.57 & 0.95 & **6.98** & **0.47** \\ \hline \multirow{5}{*}{4} & RF & 99.91 & 99.96 & 99.97 & 99.96 & 330.49 & 5.95 & 28.11 & 0.43 \\  & **SVM** & **99.99** & **99.90** & **99.99** & **99.94** & 35.33 & 3.85 & 17.46 & 1.51 \\  & KNN & 99.83 & 96.89 & 99.53 & 97.95 & **1.10** & **0.03** & 31.69 & 1.80 \\  & ELM & 99.95 & 99.30 & 99.98 & 99.59 & 6.83 & 0.87 & **5.04** & **0.96** \\ \hline \multirow{5}{*}{5} & RF & 99.20 & 94.22 & 99.68 & 99.12 & 363.29 & 9.06 & 31.29 & 1.48 \\  & SVM & 99.98 & 99.75 & 99.99 & 99.82 & 29.86 & 3.90 & 16.75 & 2.02 \\  & KNN & 99.90 & 98.48 & 99.96 & 98.98 & **1.00** & **0.02** & 27.43 & 1.23 \\  & **ELM** & **99.98** & **99.76** & **99.99** & **99.83** & 3.34 & 0.28 & **2.47** & **0.24** \\ \hline \multirow{5}{*}{6} & RF & 98.52 & 90.45 & 97.50 & 92.97 & 255.22 & 8.04 & 24.90 & 1.79 \\  & SVM & **99.07** & **92.66** & **96.15** & **93.62** & 53.89 & 3.80 & 28.18 & 1.52 \\  & KNN & 98.88 & 90.49 & 95.31 & 91.69 & **1.23** & **0.42** &it produces easily interpretable results modeled as decision trees. The performance of SVM heavily depends on the kernel function used: linear kernel is faster than RBF (see Tables 4 and 5), however, ELM trains and predicts faster even when compared to linear SVM models. Even without additional tuning, ELM showed the best accuracy comparing to all other algorithms in two classification problems, and in one showed the same accuracy as SVM and KNN. KNN outperformed all algorithms in one of the classification problems, while in seven problems SVM showed the best accuracy. Aiming to stabilize and further enhance the performance of the fastest classification algorithm, we applied differential evolution (DE) [29] and fish school search with exponential step decay (ETFSS) [50] to ELM input weights and hidden biases fine-tuning by maximizing the _Macro \(F_{1}\) score_ (10). The reasoning behind choosing these algorithms is described in Section 4.6. The experimentally selected parameters of the considered biology-inspired algorithms are listed in Table 6. The plots illustrating the convergence of the DE and ETFSS algorithms improving ELM performance in tasks with most diverse solutions are shown in Figure 6. The comparison of ELM with ELM models fine-tuned with DE (DELM) and with ELM models fine-tuned with ETFSS (FELM) is shown in Table 7.