Dynamic F1-score-based voting strategies for multi-class classification: an adaptive ensemble approach for non-linear and imbalanced datasets

Classification is a core machine learning task, and ensemble voting methods are widely used to improve predictive accuracy in domains such as medical diagnosis, where class imbalance and non-linear decision boundaries are common. Conventional strategies: Majority Voting (MV), Weighted Voting (WV), and Soft Voting (SV) rely on static or classifier-level weighting schemes that fail to capture per-class differences in classifier reliability. Three dynamic, class-specific voting strategies are introduced: Highest Class F1-Score Voting (HCF1V), Cumulative Class F1-Score Voting (CCF1V), and Enhanced Class F1-Score Voting (ECF1V), each assigning classifier weights based on per-class F1-scores obtained during validation rather than overall performance. The strategies were evaluated through computational simulation on three synthetic non-linear datasets (Gaussian Mixture, Spiral, and Moon) and two real-world medical benchmarks—the Breast Cancer Wisconsin Dataset (BCWD) and the UCI Heart Disease Dataset (UHDD)—using scikit-learn–based classifiers, with statistical significance assessed via Wilcoxon signed-rank tests. ECF1V achieved the highest accuracy across most settings, reaching 98.25% on BCWD and 89.47% on UHDD, outperforming both conventional voting methods and several recently published approaches. These results indicate that class-specific F1-score-based weighting improves ensemble reliability, particularly under class imbalance, supporting its applicability to high-stakes classification tasks such as medical diagnosis.