Gomes, Heitor MuriloRead, JesseGrzenda, MaciejPfahringer, BernhardBifet, Albert2026-06-182026-06-182026-03Gomes, H. M., Read, J., Grzenda, M., Pfahringer, B., & Bifet, A. (2026). SLEADE: Disagreement-based semi-supervised learning for sparsely labeled evolving data streams. IEEE Transactions on Knowledge and Data Engineering, 38(3), 1973-1985. https://doi.org/10.1109/TKDE.2025.36470501041-4347https://hdl.handle.net/10289/18385Semi-supervised learning (SSL) problems are challenging, appear in many domains, and are particularly relevant to streaming applications, where data are abundant but labels are not. The problem tackled here is classification over an evolving data stream where labels are rare and distributed randomly. We propose SLEADE (Stream LEArning by Disagreement Ensemble), a novel method that exploits disagreement-based learning and unsupervised drift detection to leverage unlabeled data during training. SLEADE uses pseudo-labeled instances to augment the training set of each member of an ensemble using a majority trains the minority scheme. The pseudo-labeled data impact is controlled by a weighting function that considers the confidence in the prediction attributed by the ensemble members. SLEADE exploits unsupervised drift detection, which allows the ensemble to respond to changes. We present several experiments using real and synthetic data to illustrate the benefits and limitations of SLEADE compared to existing algorithms.enThis is an accepted version of an article published in the journal IEEE Transactions on Knowledge and Data Engineering. © 2026 IEEE.computer scienceconcept driftdata streamdisagreement-based learningsemi-supervised learning (SSL)Journal Article10.1109/TKDE.2025.36470501558-21914603 Computer Vision and Multimedia Computation46 Information and Computing Sciences4611 Machine Learning46 Information and computing sciences