# Dynamic Ensemble Selection for EEG Signal Classification in Distributed Data Environments
## 3 Datasets and Experimental Results
The color-coded values indicate the average F1-weighted score, computed across all levels of data dispersion (3 to 11 local tables). As shown, Gradient Boosting combined with the unified strategy consistently achieves the highest performance, clearly outperforming the other approaches. A comparison of dynamic ensemble selection strategies with conflict analysis revealed that unified coalition approaches consistently outperformed both simple probability-based voting and diverse coalition models. While the probability sum method remained competitive in several configurations, it was outclassed by coalition-based strategies. In contrast, the diverse groups strategy, which aggregates classifiers with conflicting predictions, generally resulted in reduced classification performance across all metrics, especially when paired with weak learners or simple models. Furthermore, measurement-level fusion of classifier outputs proved superior to abstract-level fusion. Access to the full probability distribution of predictions enabled more precise and informative coalition formation and decision-making, particularly benefiting complex ensemble techniques. Importantly, the study demonstrates that the proposed framework is scalable and maintains high classification quality even with an increased number of data sources (up to 11 local tables), particularly when strong base learners are employed and appropriate ensemble strategies are selected. Figure 2 presents a line chart illustrating the variation in F1-weighted scores across different levels of data dispersion and the EEG Epilepsy dataset (i.e., the number of local tables: 3, 5, 7, 9, 11) for each classifier, based on their best-performing ensemble strategy. The results reveal that Gradient Boosting demonstrates strong robustness to data distribution, achieving its highest performance at nine local tables (LT). AdaBoost maintains consistently high performance up to 7 LT, followed by a slight decline. In contrast, Random Forest and Logistic Regression show a gradual decrease in classification quality as dispersion increases. k-Nearest Neighbors (kNN) exhibits the weakest but stable performance across all dispersion levels. Figure 1: Heat map of classifier performance by ensemble strategy and fusion level (F1-weighted) for the EEG Epilepsy dataset. Comparing the results presented in Tables 3 and 4 for the BCI Competition IV Dataset 1, one can notice small but systematic differences in the effectiveness of various approaches. Generally, the best results in both tables were obtained by AdaBoost and Gradient Boosting models, especially in combination with the measurement level and homogeneous groups. The F1 (weighted) values for these approaches ranged from approximately 0.57 to 0.59, without exceeding 0.6, which indicates a moderate difficulty of the dataset. Although the differences between the tables are subtle, Table 4--corresponding to experiments with greater data dispersion (9 LT and 11 LT)--shows slightly higher F1 values for some configurations; e.g., for diverse groups at the measurement level with AdaBoost, an F1 of 0.589 was achieved with 9 LT, while analogous approaches in Table 3 reached a maximum of 0.584 with 5 LT. On the other hand, in Table 3, methods with fewer local tables (3 LT-5 LT) often performed better, suggesting that the increase in the number of data sources affects the results--both positively and negatively, depending on the configuration. Overall, however, none of the methods clearly dominated the others, and the classification performance remained at a similar level in all tested variants. Figure 3 illustrates the comparative F1-measure for the BCI Competition IV Dataset 1 of various classifiers across different ensemble strategies and fusion levels. AdaBoost consistently achieves the highest F1-scores across all strategies, with values ranging from 0.528 to 0.579.