Multi-label classification via incremental clustering on an evolving data stream
- Submitting institution
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Robert Gordon University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- McCall_3
- Type
- D - Journal article
- DOI
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10.1016/j.patcog.2019.06.001
- Title of journal
- Pattern Recognition
- Article number
- -
- First page
- 96-113
- Volume
- 95
- Issue
- -
- ISSN
- 0031-3203
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2019
- URL
-
-
- Supplementary information
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-
- 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
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The ability to adapt classification from multiple detectors in varying time series data has significant potential for decision-making systems driven on time-varying data. McCall and Nguyen are finding application of this approach in detection of gross errors in time-varying hydrocarbon flow systems where data from an array of detectors (hydrocarbon meters) can be fused to detect reading errors or leaks: InnovateUK KTP project 11488 (£295K) - “Gross Error Detection in Hydrocarbon Systems” (2019 – 2022) .
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -