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A Survey on Ensemble Learning for Data Stream Classification
- Heitor Murilo Gomes, Jean Paul Barddal, Fabrício Enembreck, Albert Bifet
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- References
- ACM Computing Surveys (CSUR), June 2017, vol. 50, n° 2, pp. 23
- Abstract
Ensemble-based methods are among the most widely used techniques for data stream classification. Their popularity is attributable to their good performance in comparison to strong single learners while being relatively easy to deploy in real-world applications. Ensemble algorithms are especially useful for data stream learning as they can be integrated with drift detection algorithms and incorporate dynamic updates, such as selective removal or addition of classifiers. This work proposes a taxonomy for data stream ensemble learning as derived from reviewing over 60 algorithms. Important aspects such as combination, diversity, and dynamic updates, are thoroughly discussed. Additional contributions include a listing of popular open-source tools and a discussion about current data stream research challenges and how they relate to ensemble learning (big data streams, concept evolution, feature drifts, temporal dependencies, and others).
- Keywords
- Data Stream Mining, Ensemble, Ensemble Classifiers, Survey
- Category
- Article in peer reviewed Journal
- Research Area(s)
- Computer Science/Artificial Intelligence
- Identifier(s)
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DOI 10.1145/3054925
Bibliographic key gomes2017survey
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- Last update
- on february 03, 2018 by Albert Bifet
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