Author

J. P. Barddal

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Open access Aug 2026

Hoeffding adaptive splitting trees for data stream classification with concept drift and ensemble learning

These models combine the periodic splitting strategy of Hoeffding Trees, which fosters ensemble diversity, with adaptive splitting mechanisms that employ change detection algorithms to identify performance decay and determine split points.

Daniel Nowak Assis, J. P. Barddal, Fabrício Enembreck · Data mining and knowledge di... · 0 citations

#machine learning Preprint Sep 2026

Concept Drift from a Causal Perspective

Concept drift is a common phenomenon in real-world data streams, in which changes in the data-generating distribution can degrade predictive model performance. Most existing definitions characterize drift as changes in the joint distribution $P(\mathbf{x}, y)$, without distinguishing which component of the data-generat...

Eduardo V. L. Barboza, J. P. Barddal, R. Sabourin et al. · 0 citations