Ensemble learning for data stream analysis: A survey
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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].”
From A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods (2021) · cites this paper as [150]
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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
- green
- figshare.com
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- 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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