# Dynamic Ensemble Selection for EEG Signal Classification in Distributed Data Environments
## 3 Datasets and Experimental Results
The maximum depth of each individual regression estimator (max_depth) was varied within \(\{3,4,5,6\}\). * Logistic Regression: The following solvers were evaluated: lbfgs, liblinear, sag, saga, and newton-cg. The class_weight parameter was set either to balanced or none to assess the impact of handling class imbalance. Regularization was controlled via the penalty parameter, which was tested with values II, I2, and None. The tables report the best performance achieved for each configuration, along with the corresponding number of estimators that obtained this result. For each dataset, the highest-performing outcome is indicated, with the best result highlighted to facilitate comparison across different configurations. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**No. of Tables**} & \multirow{2}{*}{**Method/Model**} & \multirow{2}{*}{**Prec.**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1**} & \multicolumn{2}{c}{**F1**} \\  & & & **(Weig.)** & **(Macro)** & **BAacc** & **Acc** \\ \hline
3 LT & Abstract level; sum; RF (100,gini) & 0.903 & 0.893 & 0.892 & 0.892 & 0.893 & 0.893 \\  & Measurement level; sum; RF (200,entropy) & 0.94 & 0.927 & 0.926 & 0.926 & 0.927 & 0.927 \\  & Unified groups; abstract level; RF (100,gini) & 0.903 & 0.893 & 0.892 & 0.892 & 0.893 & 0.893 \\  & Diverse groups; abstract level; RF (100,gini) & 0.903 & 0.893 & 0.892 & 0.892 & 0.893 & 0.893 \\  & Unified groups; measurement level; RF (200,entropy) & 0.94 & 0.927 & 0.926 & 0.926 & 0.927 & 0.927 \\  & Diverse groups; measurement level; RF (200,entropy) & 0.94 & 0.927 & 0.926 & 0.926 & 0.927 & 0.927 \\  & Abstract level; sum; kNN (2) & 0.779 & 0.76 & 0.762 & 0.762 & 0.76 & 0.76 \\  & Measurement level; sum; kNN (2) & 0.746 & 0.727 & 0.723 & 0.727 & 0.727 \\  & Unified groups; abstract level; kNN (2) & 0.779 & 0.76 & 0.762 & 0.762 & 0.76 & 0.76 \\  & Diverse groups; abstract level; kNN (2) & 0.75 & 0.72 & 0.717 & 0.717 & 0.72 & 0.72 \\  & Unified groups; measurement level; kNN (2) & 0.736 & 0.72 & 0.721 & 0.721 & 0.72 & 0.72 \\  & Diverse groups; measurement level; kNN (2) & 0.712 & 0.68 & 0.683 & 0.683 & 0.68 & 0.68 \\  & Abstract level; sum; AdaBoost (50) & 0.946 & 0.933 & 0.932 & 0.932 & 0.933 & 0.933 \\  & Measurement level; sum; AdaBoost (50) & 0.946 & 0.933 & 0.932 & 0.932 & 0.933 & 0.933 \\  & Unified groups; abstract level; AdaBoost (50) & 0.938 & 0.927 & 0.926 & 0.926 & 0.927 & 0.927 \\  & Unified groups; measurement level; AdaBoost (50) & 0.946 & 0.933 & 0.932 & 0.932 & 0.933 & 0.933 \\  & Diverse groups; measurement level; AdaBoost (50) & 0.946 & 0.933 & 0.932 & 0.932 & 0.933 & 0.933 \\  & Unified groups; measurement level; AdaBoost (50) & 0.946 & 0.933 & 0.932 & 0.932 & 0.933 & 0.933 \\  & Abstract level; sum; Gradient Boosting (100) & 0.972 & 0.967 & 0.966 & 0.966 & 0.967 & 0.967 \\  & Measurement level; sum; Gradient Boosting (50) & 0.961 & 0.953 & 0.953 & 0.953 & 0.953 & 0.953 \\  & Unified groups; abstract level; Gradient Boosting (50) & 0.983 & 0.98 & 0.98 & 0.98 & 0.98 & 0.98 \\  & Diverse groups; abstract level; Gradient Boosting (50) & 0.839 & 0.827 & 0.823 & 0.823 & 0.827 & 0.827 \\  & Unified groups; measurement level; Gradient Boosting (50) & 0.961 & 0.953 & 0.953 & 0.953 & 0.953 & 0.953 \\  & Diverse groups; measurement level; Gradient Boosting (200) & 0.902 & 0.893 & 0.892 & 0.892 & 0.893 & 0.893 \\  & Abstract level; sum; Logistic Regression & 0.878 & 0.873 & 0.874 & 0.874 & 0.873 & 0.873 \\  & Measurement level; sum; Logistic Regression & 0.899 & 0.893 & 0.893 & 0.893 & 0.893 & 0.893 \\  & Unified groups; abstract level; Logistic Regression & 0.878 & 0.873 & 0.874 & 0.874 & 0.873 & 0.873 \\  & Diverse groups; abstract level; Logistic Regression & 0.832 & 0.807 & 0.809 & 0.809 & 0.807 & 0.807 \\  & Unified groups; measurement level; Logistic Regression & 0.899 & 0.893 & 0.893 & 0.893 & 0.893 \\  & Diverse groups; measurement level; Logistic Regression & 0.87 & 0.853 & 0.855 & 0.855 & 0.853 & 0.853 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of precision (Prec.