EACD: Evolutionary Adaptation to Concept Drifts in Data Streams
- Submitting institution
-
Birmingham City University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11Z_OP_D0041
- Type
- D - Journal article
- DOI
-
10.1007/s10618-019-00614-6
- Title of journal
- Data Mining and Knowledge Discovery
- Article number
- -
- First page
- 663
- Volume
- 33
- Issue
- -
- ISSN
- 1384-5810
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2019
- URL
-
https://link.springer.com/article/10.1007/s10618-019-00614-6
- Supplementary information
-
-
- 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
-
-
- Research group(s)
-
-
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The proposed method outperforms many state-of-the-art data stream mining algorithms on synthetic and a variety of real datasets. It was recommended in a survey paper (Manickaswamy, T. and A, D.B. 2020. CONCEPT DRIFT IN DATA STREAM CLASSIFICATION USING ENSEMBLE METHODS: TYPES, METHODS AND CHALLENGES. INFOCOMP Journal of Computer Science. 19, 2 (Dec. 2020), 163-174), acknowledged by other related studies (e.g. https://doi.org/10.1016/j.eswa.2019.113069; https://doi.org/10.1007/s00521-020-05386-5; https://doi.org/10.1109/FUZZ48607.2020.9177566; https://doi.org/10.3390/info12010024) and laid foundation for a more accurate data stream mining method (https://doi.org/10.1109/ACCESS.2019.2954993).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -