EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments
- Submitting institution
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University of Ulster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 76629857
- Type
- D - Journal article
- DOI
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10.1016/j.patcog.2014.07.028
- Title of journal
- Pattern Recognition
- Article number
- -
- First page
- 659
- Volume
- 48
- Issue
- 3
- ISSN
- 0031-3203
- Open access status
- Out of scope for open access requirements
- Month of publication
- August
- Year of publication
- 2014
- 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
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2
- Research group(s)
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A - Intelligent Systems Research Centre
- Citation count
- 31
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- <24> At the time of publication, these were the only tests available for covariate shift detection in streaming data and the methods have recently been adopted by the Ikerlan Technology Research Centre (ITRC), University of Granada in developing an R package: otsad (https://cran.r-project.org/web/packages/otsad/index.html), reported in a Neurocomputing article (doi.org/10.1016/j.neucom.2019.09.032). Using these methods, an adaptive brain-computer interface for stroke rehabilitation has been developed (10.1109/TCDS.2017.2787040) and linked to impact case study 2 (ICS2) (REF2021). This contributed to Raza’s PhD award in 2016, and appointment to Lectureship at Essex University, and Li being appointed to Senior Lecturer position in Cardiff University in 2018.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -