Scalable real-time classification of data streams with concept drift
- Submitting institution
-
The University of Reading
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 70047
- Type
- D - Journal article
- DOI
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10.1016/j.future.2017.03.026
- Title of journal
- Future Generation Computer Systems
- Article number
- -
- First page
- 187
- Volume
- 75
- Issue
- -
- ISSN
- 0167-739X
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2017
- URL
-
-
- 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
-
3
- Research group(s)
-
9 - DSAI
- Citation count
- 31
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The significance of this paper is that it develops the first real-time and parallel data stream classifier, capable of instance-by-instance adaptation. No existing algorithm at the time of publication offered both capabilities. The publication directly led to an invited keynote talk at the 32nd European Conference on Modelling and Simulation (http://www.scs-europe.net/conf/ecms2018/invited.html).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -