The DOI matches its Crossref record; 220 of the 220 references it cites were found in a registry or an index.
Ensemble learning for data stream analysis: A survey
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In the citing paper
“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]
In the citing paper
“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]
Citations
Cites
Items 201–220 of 220
A new ensemble method for multi-label data stream classification in non-stationary environment
Song, G., & Ye, Y. (2014) · doi:10.1109/ijcnn.2014.6889846 · Cited by 9
Survival analysis on data streams: Analyzing temporal events in dynamically changing environments
Shaker, A., & Hüllermeier, E. (2014) · International Journal of Applied Mathematics and Computer Science · doi:10.2478/amcs-2014-0015 · Cited by 9
Hybrid active learning for non-stationary streaming data with asynchronous labeling
Kim, H., Madhvanath, S., & Sun, T. (2015) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2015.7363766 · Cited by 9
Semi-supervised approach to handle sudden concept drift in Enron data
Kmieciak, M. R., & Stefanowski, J. (2011) · Control and Cybernetics · Cited by 8
Self-citationComparing Block Ensembles for Data Streams with Concept Drift
Deckert, M., & Stefanowski, J. (2012) · Advances in intelligent systems and computing · doi:10.1007/978-3-642-32518-2_7 · Cited by 7
Self-citationComparison of Tree-Based Methods for Multi-target Regression on Data Streams
Osojnik, A., Panov, P., & Džeroski, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-39315-5_2 · Cited by 7
Altıntaş, İ., Norman, M. D., Dongarra, J. J., et al. (2016) · Data Archiving and Networked Services (DANS) · Cited by 7
An approach for online learning in the presence of concept changes
Jaber, G. (2013) · doi:10.70675/73ffbe13za251z4148zad13z186542d8679c · Cited by 6
Zhou, Z., & Washio, T. (2009) · doi:10.1007/978-3-642-05224-8 · Cited by 5
Active Learning Classifier for Streaming Data
Woźniak, M., Cyganek, B., Kasprzak, A., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-32034-2_16 · Cited by 5
Self-citationPfahringer, B., Holmes, G., & Hoffmann, A. G. (2010) · doi:10.1007/978-3-642-16184-1 · Cited by 4
Adaptive Splitting and Selection Algorithm for Regression
Jackowski, K. (2015) · New Generation Computing · doi:10.1007/s00354-015-0405-1 · Cited by 4
A First Attempt on Online Data Stream Classifier Using Context
Woźniak, M., & Cyganek, B. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-40973-3_50 · Cited by 4
Self-citationTheeramunkong, T., Nattee, C., Adeodato, P. J. L., et al. (2010) · Springer eBooks · Cited by 3
Classifiers for Concept-drifting Data Streams: Evaluating Things That Really Matter
Brzeziński, D., & Stefanowski, J. (2013) · Cited by 3
Self-citationIncremental One-Class Bagging for Streaming and Evolving Big Data
Krawczyk, B., & Woźniak, M. (2015) · Trust, Security And Privacy In Computing And Communications · doi:10.1109/trustcom-bigdatase-ispa.2015.582 · Cited by 2
Self-citationAdaptive Ensembles for Evolving Data Streams – Combining Block-Based and Online Solutions
Stefanowski, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-39315-5_1 · Cited by 2
Self-citationFramework for Storing and Processing Relational Entities in Stream Mining
Matuszyk, P., & Spiliopoulou, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37456-2_42 · Cited by 1
Omitaomu, O. A., Ganguly, A. R., da Gama, J. M. P., et al. (2009) · Knowledge Discovery and Data Mining · Cited by 1
Self-citation
Flagged first, then by citations
Research works printed but not linked: 4
- Kmieciak, 2011, Handling sudden concept drift in Enron message data streams, Control Cybern., 40, 667
- F. Roli, G. Giacinto, Design of Multiple Classifier Systems, World Scientific Publishing.
- andLuis Torgo, 2016, A survey of predictive modeling under imbalanced distributions, ACM Comput. Surv., 49, 31:1
- I. Zliobaite, How good is the electricity benchmark for evaluating concept drift adaptation, arXiv preprint arXiv:1301.3524(2013b). · arXiv
Cited by
Sorted by citations
A comprehensive review on ensemble deep learning: Opportunities and challenges
Mohammed, A., & Kora, R. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.01.014 · Cited by 1,127
Learning under Concept Drift: A Review
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., & Zhang, G. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2876857 · Cited by 1,603
Self-citationO’Dwyer, E., Pan, I., Acha, S., & Shah, N. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.024 · Cited by 410
A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods
Yao, L., Xu, S., Tang, A., Zhou, F., Hou, J., Xiao, Y., & Fu, Z. (2021) · World Electric Vehicle Journal · doi:10.3390/wevj12030113 · Cited by 237
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336
Metadata
- 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 › Information Systems › 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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