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cs.LG2025
Ensemble Performance Through the Lens of Linear Independence of Classifier Votes in Data Streams
Enes Bektas, Fazli Can
Ensemble learning improves classification performance by combining multiple base classifiers. While increasing the number of classifiers generally enhances accuracy, excessively la…
cs.LG2025★ 1 cited
LSH-DynED: A Dynamic Ensemble Framework with LSH-Based Undersampling for Evolving Multi-Class Imbalanced Classification
Soheil Abadifard, Fazli Can
The classification of imbalanced data streams, which have unequal class distributions, is a key difficulty in machine learning, especially when dealing with multiple classes. While…