## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Ensemble** & **Feature Selection** & \begin{tabular}{c} **Average of** \\ **Descriptors** \\ \end{tabular} & **Average Accuracy** & **Average AUC** & \
\begin{tabular}{c} **Top 1** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **Top 10** \\ **Accuracy** \\ \end{tabular} \\ \hline CR & DL & ReliefF + bGWO & 21 \(\pm\) 3 & 99.76\% \(\pm\) 0.30 & 1 & 100\% & 99.39\% \\ OED & HFs & ReliefF + PSO & 11 \(\pm\) 3 & 100\% & 1 & 100\% & 100\% \\ LA & HFs & ReliefF + bGWO & 40 \(\pm\) 7 & 98.73\% \(\pm\) 0.36 & 0.999 \(\pm\) 0.4 \(\times\) 10\({}^{-3}\) & 99.24\% & 98.11\% \\ LG & HFs+DL & ReliefF + bGWO & 29 \(\pm\) 6 & 99.47\% \(\pm\) 0.30 & 1 & 100\% & 98.87\% \\ NHL & HFs+DL & ReliefF + GA & 53 \(\pm\) 5 & 90.72\% \(\pm\) 1.04 & 0.980 \(\pm\) 1.5 \(\times\) 10\({}^{-3}\) & 92.25\% & 89.57\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Main combinations in each H&E-stained histological dataset based on the criterion of higher accuracy with the lowest number of descriptors. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ensemble** & **Wrapper** & **CR** & **OED** & **LA** & **LG** & **NHL** \\ \hline \multirow{4}{*}{HFs} & PSO & 91.94\% & **100\%** & 98.30\% & 99.36\% & 78.02\% \\  & GA & 91.39\% & 99.66\% & 98.58\% & 99.36\% & 77.59\% \\  & bGWO & 92.30\% & 99.66\% & **98.73\%** & 99.36\% & 77.94\% \\  & PSO & 99.45\% & 96.89\% & 93.50\% & 98.75\% & 90.64\% \\ \multirow{2}{*}{DL} & GA & 99.15\% & 97.64\% & 93.01\% & 98.75\% & 90.56\% \\  & bGWO & **99.76\%** & 96.96\% & 93.39\% & 98.87\% & 90.13\% \\  & PSO & 99.58\% & 98.45\% & 96.86\% & 99.40\% & 90.51\% \\ \multirow{2}{*}{HFs+DL} & GA & 99.45\% & 98.45\% & 96.72\% & 99.36\% & **90.72\%** \\  & bGWO & 99.45\% & 99.05\% & 96.44\% & **99.47\%** & 90.43\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average accuracy values obtained through the proposed approach with two-stage feature selection (ReliefF + Wrapper). datasets, with expressive average AUC rates of 0.999 (LA) and 1 (CR and LG). In addition, the lowest top 10 accuracy was significant, with a rate above 98% (LA), on the dataset that involves four groups for the classification process. Moreover, the main solutions for these three datasets indicated a reduced number of descriptors, an average of 21 features for CR, 29 for LG, and 40 for LA. Regarding the two-stage ReliefF \(+\) PSO, this strategy was the main solution for the OED dataset, providing maximum accuracy with the lowest number of descriptors among all solutions with only 11 features. When the two-stage ReliefF \(+\) GA is observed, this approach constitutes the best solution on a single dataset (NHL). In this case, the solutions explored 53 features on average, identifying the highest value among all solutions. The top 10 accuracy was 89.57%, and the top 1 was slightly better, 92.25%, reinforcing the difficulties present in this set. The NHL dataset comprises three classes (CLL\(\times\)FL\(\times\)MCL) and, possibly, with less heterogeneous histological patterns, implying more difficulties in the constitutions of the solutions. Even so, in this case, the average AUC was 0.98, which is an important value under the exposed conditions. For instance, considering that the original feature space had a range of 462 to 11,086 values, the outcome achieved in this study is another contribution that is capable of providing expressive average performances with few descriptors but highly relevant to the classification process.