# Dynamic Ensemble Selection for EEG Signal Classification in Distributed Data Environments
## 3 Datasets and Experimental Results
The diverse-measurement fusion level brings the best overall score for AdaBoost (0.579), suggesting that incorporating diverse feature sources at the measurement level benefits this classifier the most. Gradient Boosting also performs strongly, with scores clustering around 0.543-0.557. The best result (0.557) is also seen under the diverse-measurement condition. Logistic Regression and Random Forest show more stable but slightly lower performance. Their F1-scores fluctuate minimally across strategies, indicating that their performance is less sensitive to the fusion strategy, with Logistic Regression the highest at 0.536 and Random Forest at 0.530. k-Nearest Neighbors yields the lowest performance, particularly under sum-abstract and unified-abstract, where the F1-score drops to 0.376. However, performance improves substantially under diverse-abstract and diverse-measurement strategies (0.508 and 0.501, respectively), highlighting kNN's benefit from diversity in feature fusion. Figure 4 presents a line chart illustrating the variation in F1-weighted scores across different levels of data dispersion and the BCI Competition IV Dataset 1 (i.e., the number of local tables: 3, 5, 7, 9, 11) for each classifier, based on their best-performing ensemble strategy. Figure 2: Classifier performance by ensemble strategy and fusion level (F1-weighted) for the EEG Epilepsy dataset. Among the classifiers, AdaBoost demonstrates the most consistently high performance across all dispersion levels. This stability indicates that AdaBoost is robust to distributional changes and can maintain reliable predictive quality even in more fragmented learning environments. Gradient Boosting starts with strong performance at three local tables, close to that of AdaBoost, but exhibits a gradual decline as the data become more dispersed. Random Forest shows relatively flat performance across all levels of dispersion. While not severely impacted by the increase in local tables, its performance does not reach the levels of the boosting methods, highlighting its limitations in capturing complex patterns in this BCI dataset under decentralized conditions. Logistic Regression exhibits more variability. Finally, k-Nearest Neighbors consistently achieves the lowest F1-weighted scores, although a slight upward trend is visible from three to seven local tables, after which performance plateaus. Overall, the results indicate that AdaBoost offers the most robust classification capability under distributed conditions, followed by Gradient Boosting, while Random Forest and kNN are less effective for this task. Statistical tests were performed to confirm the observations and F1-weighted values were used for comparison. The F1-weighted values of all six approaches were compared: 1--Abstract level, sum; 2--Measurement level, sum; 3--Unified groups, abstract level; 4--Diverse groups, abstract level; 5--Unified groups, measurement level; 6--Diverse  
Figure 4: Classifier performance by ensemble strategy and fusion level (F1-weighted) for the BCI Competition IV Dataset 1. Figure 3: Heat map of classifier performance by ensemble strategy and fusion level (F1-weighted) for the BCI Competition IV Dataset 1.  
groups, measurement level. So, six dependent samples, each containing 50 observations, were created, representing the results presented in Tables 1-4. The Friedman test was used to determine whether the differences in F1-weighted values among the approaches were statistically significant. The Friedman test indicated that there is a statistically significant difference in mean F1-weighted among the twenty approaches, \(\chi^{2}(50,5)=14.24,p\ =\ 0.014\). To pinpoint the specific differences, a post hoc Dunn-Bonferroni test was performed, with the significant results highlighted in blue in Table 5. The test revealed significant differences between the diverse groups, abstract level approach, and the diverse groups, measurement level approach.