aceDynamic 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 Cl…
Dynamic class-specific F1-score weighting with ECF1V increased ensemble classification accuracy to 98.25% on Breast Cancer Wisconsin and 89.47% on UCI Heart Disease datasets, outperforming conventional voting under non-linear and imbalanced conditions.
Evidence
- Peer-reviewedScientific Reports2026-08-25
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Truvace Impact Record TRV-2026-0895, v1: “Dynamic F1-score-based voting strategies for multi-class classification: an adaptive ensemble approach for non-linear and imbalanced datasets.” Truvace, 2026-08-26. /record/TRV-2026-0895 (accessed at citation time). sha256 bd5dec98dabfdfb7…
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