## 5 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **BIN-NLCEE** & **NLCEE** & **CEE** \\ \hline breast & 98.45 \(\pm\) 0.28 & 98.09 \(\pm\) 0.33 & 97.46 \(\pm\) 0.33 \\ (B) & & & \\ diabetes & 83.34 \(\pm\) 0.65 & 82.00 \(\pm\) 0.65 & 80.92 \(\pm\) 0.66 \\ (tested\_negative) & & & \\ Parkinson’s & 99.27 \(\pm\) 0.33 & 98.53 \(\pm\) 0.42 & 97.92 \(\pm\) 0.51 \\ (healthy) & & & \\ mammography & 85.86 \(\pm\) 0.65 & 85.64 \(\pm\) 0.64 & 85.23 \(\pm\) 0.63 \\ (benign) & & & \\ COVID-19 & 64.56 \(\pm\) 0.39 & 64.22 \(\pm\) 0.41 & 63.52 \(\pm\) 0.39 \\ (tested\_negative) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Fitness values for negative cases of each dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **BIN-NLCEE** & **NLCEE** & **CEE** \\ \hline breast & 97.89 \(\pm\) 0.35 & 97.18 \(\pm\) 0.37 & 96.45 \(\pm\) 0.42 \\ (M) & & & \\ diabetes & 82.34 \(\pm\) 0.64 & 80.93 \(\pm\) 0.66 & 80.21 \(\pm\) 0.72 \\ (tested\_positive) & & & \\ Parkinson’s & 97.34 \(\pm\) 0.55 & 95.78 \(\pm\) 1.12 & 96.13 \(\pm\) 0.72 \\ (PD) & & & \\ mammography & 84.23 \(\pm\) 0.59 & 84.28 \(\pm\) 0.64 & 83.78 \(\pm\) 0.62 \\ (malignant) & & & \\ COVID-19 & 65.06 \(\pm\) 0.39 & 63.62 \(\pm\) 0.34 & 62.91 \(\pm\) 0.37 \\ (tested\_positive) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Fitness values for positive cases of each dataset. results compared to all other methods. BIN-NLCEE obtained an average fitness of 65.06 (64.67 to 65.45), NLCEE obtained an average fitness of 63.62 (63.28 to 63.96), and CEE obtained an average fitness of 62.91 (62.54 to 63.28). Thus, in 20 comparisons, the BIN-NLCEE achieved better results in 9 (45%) and, according to the confidence interval, equal results in 11 (55%). In this way, the BIN-NLCEE was equal to or better than NLCEE and CEE in 100% of the comparisons, not being worse in any results. Table 9 shows the results obtained by four traditional methods used for the classification task: J48 [24], IBK [25], naive Bayes (NB) [26], and SVM [27]. These methods were run using Weka [28], a machine learning and data mining open-source software. To make a fair comparison, the methods were evaluated using the same evaluation method as the GAs, shown in Equation (3). The first column presents the dataset name and the remaining ones; the results are in the same format as Tables 7 and 8. For the breast-w dataset, SVM achieved better results, with a fitness of 97.02 (ranging from 96.96 to 97.08), while J48, IBK, and NB obtained 92.94 (92.77 to 93.11), 95.07 (from 94.99 to 95.15), and 92.07 (92.01 to 92.13), respectively. NB achieved better results in the diabetes dataset, with fitness of 72.11 (ranging from 72.00 to 72.22), while J48, IBK, and SVM obtained fitness levels of 70.66 (70.41 to 70.91), 66.65 (66.49 to 66.81), and 71.38 (71.28 to 71.48), respectively. For the Parkinson's dataset, IBK obtained better results, with fitness of 95.56 (ranging from 95.34 to 95.78). J48 achieved the second best result, with a fitness of 79.70 (from 79.09 to 80.31). NB and SVM obtained 76.80 (from 76.59 to 77.01) and 74.94 (from 74.73 to 75.15). In the mammography dataset, J48 obtained better results, with fitness of 81.57 (from 81.46 to 81.68), while IBK, NB, and SVM obtained fitness levels of 74.68 (74.53 to 74.83), 77.80 (77.70 to 77.90), and 79.65 (79.56 to 79.74). Finally, for the COVID-19 dataset, naive Bayes obtained better results, with fitness 57.69 (from 57.60 to 57.78), while J48, IBK, and SVM obtained fitness levels of 56.39 (from 56.26 to 56.52), 54.89 (from 54.78 to 55.00), and 50.15 (from 50.08 to 50.22), respectively. BIN-NLCEE generates two results, one per class, as shown in Tables 7 and 8. Due to this and the medical domain of the data, we choose the results found for positive samples (shown in Table 8). Table 10 shows the fitness difference between BIN-NLCEE and traditional classifiers. In Table 10, the smallest difference is shown in column \(S\), and the biggest difference is shown in column \(B\).