# Dynamic Ensemble Selection for EEG Signal Classification in Distributed Data Environments
## 3 Datasets and Experimental Results
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline
**No. of Tables** & **Method/Model** & & & \multicolumn{2}{c}{**F1**} & \multicolumn{2}{c}{**F1**} & \multicolumn{2}{c}{} \\  & **Prec.** & **Recall** & **(Weig.)** & **(Macro)** & **BAacc** & **Acc** \\ \hline
3 LT & Abstract level; sum; RF (10,gini) & 0.533 & 0.533 & 0.532 & 0.532 & 0.533 & 0.533 \\  & Measurement level; sum; RF (50,entropy) & 0.52 & 0.519 & 0.518 & 0.518 & 0.519 & 0.519 \\  & Unified groups; abstract level; RF (10,gini) & 0.533 & 0.533 & 0.532 & 0.532 & 0.533 & 0.533 \\  & Diverse groups; abstract level; RF (10,entropy) & 0.523 & 0.523 & 0.522 & 0.522 & 0.523 & 0.523 \\  & Unified groups; measurement level; RF (50,entropy) & 0.52 & 0.519 & 0.518 & 0.518 & 0.519 & 0.519 \\  & Diverse groups; measurement level; RF (50,gini) & 0.521 & 0.52 & 0.518 & 0.518 & 0.52 & 0.52 \\  & Abstract level; sum; kNN (2) & 0.467 & 0.484 & 0.408 & 0.484 & 0.484 \\  & Measurement level; sum, kNN (4) & 0.473 & 0.474 & 0.466 & 0.466 & 0.474 & 0.474 \\  & Unified groups; abstract level; kNN (2) & 0.467 & 0.484 & 0.408 & 0.408 & 0.484 & 0.484 \\  & Diverse groups; abstract level; kNN (2) & 0.528 & 0.519 & 0.478 & 0.478 & 0.519 & 0.519 \\  & Unified groups; measurement level; kNN (4) & 0.468 & 0.468 & 0.467 & 0.467 & 0.468 & 0.468 \\  & Diverse groups; measurement level; kNN (4) & 0.483 & 0.484 & 0.483 & 0.483 & 0.484 & 0.484 \\  & Abstract level; sum; AdaBoost (10) & 0.574 & 0.571 & 0.566 & 0.566 & 0.571 & 0.571 \\  & Measurement level; sum; AdaBoost (200) & 0.582 & 0.58 & 0.578 & 0.578 & 0.58 & 0.58 \\  & Unified groups; abstract level; AdaBoost (10) & 0.574 & 0.571 & 0.566 & 0.566 & 0.571 & 0.571 \\  & Diverse groups; abstract level; AdaBoost (10) & 0.555 & 0.554 & 0.553 & 0.553 & 0.554 & 0.554 \\  & Unified groups; measurement level; AdaBoost (200) & 0.582 & 0.58 & 0.578 & 0.578 & 0.58 & 0.58 \\  & Diverse groups; measurement level; AdaBoost (10) & 0.581 & 0.58 & 0.578 & 0.578 & 0.58 & 0.58 \\  & Abstract level; sum; Gradient Boosting (20) & 0.586 & 0.586 & 0.585 & 0.585 & 0.586 & 0.586 \\  & Measurement level; sum; Gradient Boosting (20) & 0.561 & 0.56 & 0.559 & 0.559 & 0.56 & 0.56 \\  & Unified groups; abstract level; Gradient Boosting (20) & 0.586 & 0.586 & 0.585 & 0.585 & 0.586 & 0.586 \\  & Diverse groups; abstract level; Gradient Boosting (20) & 0.556 & 0.555 & 0.554 & 0.554 & 0.555 & 0.555 \\  & Unified groups; measurement level; Gradient Boosting (20) & 0.561 & 0.56 & 0.559 & 0.559 & 0.56 & 0.56 \\  & Diverse groups; measurement level; Gradient Boosting (20) & 0.558 & 0.556 & 0.554 & 0.554 & 0.556 & 0.556 \\  & Abstract level; sum; Logistic Regression & 0.537 & 0.536 & 0.536 & 0.536 & 0.536 & 0.536 \\  & Measurement level; sum; Logistic Regression & 0.519 & 0.519 & 0.519 & 0.519 & 0.519 & 0.519 \\  & Unified groups; abstract level; Logistic Regression & 0.537 & 0.536 & 0.536 & 0.536 & 0.536 & 0.536 \\  & Diverse groups; abstract level; Logistic Regression & 0.542 & 0.542 & 0.541 & 0.541 & 0.542 & 0.542 \\  & Unified groups; measurement level; Logistic Regression & 0.519 & 0.519 & 0.519 & 0.519 & 0.519 & 0.519 \\  & Diverse groups; measurement level; Logistic Regression & 0.544 & 0.544 & 0.543 & 0.543 & 0.544 & 0.544 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of precision (Prec. ), recall, F-measure (F-m.), balanced accuracy (_bacc_), and classification accuracy (_acc_) for the considered approaches for the BCI Competition IV Dataset 1 **Part 1**. RF is the abbreviation Random Forest and kNN for k-Nearest Neighbors. (Blue text color represent best result). \begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multicolumn{1}{c}{**No. of Tables**} & \multicolumn{1}{c}{**Method/Model**} & \multicolumn{5}{c}{**F1**} & \multicolumn{1}{c}{**F1**} \\  & & **Prec. ** & **Recall** & **(Weig.