Cited by 1,050Data Stream Mining TechniquesMachine Learning and ELMAdvanced Database Systems and Queries

Ensemble learning for data stream analysis: A survey

Open access
Krawczyk, Bartosz, Minku, Leandro L., Gama, João, Stefanowski, Jerzy, Woźniak, Michał·2017·Information Fusion

Integrity signals

Registry

Crossref · 10.1016/j.inffus.2017.02.004

Cites

220 of 220 references verified0 citation errors · 0 not checkable

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  • Smart cities require the integration of multiple large and heterogeneous sources of data, for which ML can be a valuable tool, including data matching, data fusion, and ensemble learning.

    “Smart cities require the integration of multiple large and heterogeneous sources of data, for which ML can be a valuable tool, which includes data matching [89,190], data fusion [885], and ensemble learning [451].”

    From Tackling climate change with machine learning (2023) · cites this paper as [451]

  • Ensemble learning is a combination of multiple data-driven methods, which combines multiple individual learners to achieve the purpose of estimation and prediction.

    “Ensemble learning (EL) is a combination of multiple data-driven methods, which combines multiple individual learners to achieve the purpose of estimation and prediction [150-152].”

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Authors
Krawczyk, B., Minku, L. L., Gama, J., Stefanowski, J., & Woźniak, M.
Affiliations
  • Virginia Commonwealth University (US)
  • University of Leicester (GB)
  • Universidade do Porto (PT)
  • Poznań University of Technology (PL)
  • Wrocław University of Science and Technology (PL)
Corresponding author
Krawczyk, Bartosz
Venue
Information Fusion
Publisher
Elsevier BV
Published
2017-09
Volume
37
Pages
132-156
ISSN
1566-2535 (ISSN-L)
OpenAlex
W2585528949
Cited by
1,050
References
220
Citations by year
2026: 50 · 2025: 90 · 2024: 111 · 2023: 130 · 2022: 120 · 2021: 149 · 2020: 141 · 2019: 131 · 2018: 92 · 2017: 36
FWCI
65.25
Type
journal article
Language
en
Open access
Locations
  • Information Fusion (publishedVersion)
  • VCU Scholars Compass (Virginia Commonwealth University) (submittedVersion)
  • Figshare (submittedVersion, cc-by-nc-nd)
  • Leicester Research Archive (University of Leicester) (submittedVersion, cc-by-nc-nd)
  • University of Birmingham Research Portal (University of Birmingham) (submittedVersion, cc-by-nc-nd)
  • Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
Funders
Polish National Science (DEC-2013/09/B/ST6/02264, DEC-2013/11/B/ST6/00963)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
  • Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Database Systems and Queries
Keywords
ensemble learning, data stream mining, concept drift, non-stationary data, stream classification, imbalanced data streams, novelty detection, active learning (machine learning), semi-supervised learning, structured output, incremental learning

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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