## 5 Results Analysis  
As an initial step, we replicated the experimental evaluation presented in [4], using the same 13 selected features and sticking to the same classification processes and software \begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Description** & **Values** \\ \hline penalty & Weight for reduction goal in fitness & 0.3, 0.5, 0.7 \\ var\_penalty & Variance penalty applied & Yes, No \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameter values tested for the genetic algorithm. Figure 4: Illustration of one iteration of the nested cross-validation. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline P\_crossover & Crossover probability & 0.75 \\ P\_mutation & Mutation probability & 0.15 \\ eiltims & Number of best individuals kept for next generation & 0 \\ init\_prob & Probability initial features & 0.01 \\ population & Number of individuals in each population & 50 \\ N & Number of generations to evolve & 300 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter values fixed in the genetic algorithm. for all classifiers, including the \(k\)NN classifier. This replication ensures methodological consistency and enables a direct comparative analysis with the original results. The only difference is that we transformed the 100 repetitions into a second-level 100-fold cross-validation by prescinding one or two instances per repetition. Given the small number of omitted instances, their impact on the results is expected to be negligible, while it establishes a confident approach to evaluate potential improvements and assess the robustness of the models. The results achieved using Recursive Feature Elimination (RFE) for each classifier in the original experimentation are shown in the first column of Table 6. Then, they performed a second RFE and selected 13 features as the most relevant. This is shown in the second column and it is the part of the experimentation reproduced. The classification results obtained with this subset of features in the reproduced experimentation are shown in the following columns. It is unclear to us what the value reported in the original proposal is. We believe it must be the average test accuracy of the best model found, not the average of the 100 runs over the average test, because that value is closer to the best model column in Table 6 (which is the best model according to the average of 100 repetitions of 10-fold CV test accuracies). With that consideration, the results obtained seem similar, confirming the validity of their experimentation. Nevertheless, it is important to note that validation indicates that the model's expected accuracy is around 74%, which is lower than the reported best test average of 79.63% from the original study (and the 77.65% from the reproduced equivalent result). It seems that, by repeating the train-test process many times, the feature selection and the parameters of the classifiers may be overfit to the test data. The DTC classifier achieved the highest validation accuracy (74.09%), demonstrating its effectiveness when trained with the selected features. However, the performance drop observed for the remaining classifiers between testing and validation suggests that the selected characteristics may primarily favor the DTC, potentially limiting its generalization across different models. Furthermore, it is important to note that the performance of the other classifiers does not exceed the baseline of the majority class, indicating that these models provide very poor discriminative power. To highlight this, those models with validation accuracy over the majority rate are colored in green. These results highlight the need for further analysis of the feature selection and learning process to ensure robustness and generalization. In the second phase of our study, the GA is used to perform feature selection and evaluate its impact on the different classifiers. As an example, the evolution process of one run of the GA is illustrated in Figure 5.