Combining block-based and online methods in learning ensembles from concept drifting data streams
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Publication:903664
DOI10.1016/j.ins.2013.12.011zbMath1328.68158OpenAlexW2115677675MaRDI QIDQ903664
Jerzy Stefanowski, Dariusz W. Brzeziński
Publication date: 14 January 2016
Published in: Information Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ins.2013.12.011
Related Items (10)
Online reliable semi-supervised learning on evolving data streams ⋮ Adaptiveness and consistency of a class of online ensemble learning algorithms ⋮ Classification of high-dimensional evolving data streams via a resource-efficient online ensemble ⋮ Online primal-dual learning for a data-dependent multi-kernel combination model with multiclass visual categorization applications ⋮ Exploring complex and big data ⋮ Recurring concept memory management in data streams: exploiting data stream concept evolution to improve performance and transparency ⋮ An ensemble extreme learning machine for data stream classification ⋮ How to adjust an ensemble size in stream data mining? ⋮ Kappa updated ensemble for drifting data stream mining ⋮ ROSE: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams
Uses Software
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- Combining Pattern Classifiers
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- Advances in Artificial Intelligence – SBIA 2004
- Evaluating Learning Algorithms
- Maintaining time-decaying stream aggregates
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