Online Ensemble Learning of Data Streams with Gradually Evolved Classes
- Submitting institution
-
The University of Leicester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2391
- Type
- D - Journal article
- DOI
-
10.1109/TKDE.2016.2526675
- Title of journal
- IEEE Transactions on Knowledge and Data Engineering
- Article number
- 6
- First page
- 1532
- Volume
- 28
- Issue
- 6
- ISSN
- 1041-4347
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
https://doi.org/10.1109/TKDE.2016.2526675
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 51
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Initiates the study of on-line classification of streaming data when class evolution happens gradually and provides a novel ensemble-based approach (CBCE) for this problem. This work is frequently mentioned in surveys of on-line learning/data stream analysis (Krawczyk et al. Information Fusion, 2017, Ramirez-Gallego et al. Neurocomputing, 2017, Lu et al. IEEE TKDE 2019). It has influenced subsequent work including Wang et al. (IJCAI 2017), who build upon CBCE, Lu et al. (IJCAI 2107), who use CBCE as “state-of-the art” for comparison and Nguyen et al. (Pattern Rec. 2018) who use techniques from the paper.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -