Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles
- Submitting institution
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The University of East Anglia
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 182619768
- Type
- D - Journal article
- DOI
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10.1109/TKDE.2015.2416723
- Title of journal
- IEEE Transactions on Knowledge and Data Engineering
- Article number
- -
- First page
- 2522
- Volume
- 27
- Issue
- 9
- ISSN
- 1041-4347
- Open access status
- Out of scope for open access requirements
- Month of publication
- September
- Year of publication
- 2015
- 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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3
- Research group(s)
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-
- Citation count
- 126
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The COTE algorithm established a new state-of-the-art for time series classification, beating the accepted gold standard at the time (dynamic time warping) and over a dozen algorithms proposed in the five years prior to COTE’s publication. It was the outcome of the first stage of an ongoing, long-term research collaboration on time series classification with international partners based in UK, US, Ireland, France, Brazil, Germany and Australia. It has proved effective in a range of applications, including insect classification, detecting electric devices in baggage, and detecting forged whisky, and was a key support argument for the successful EPSRC grant [EP/M015807/1].
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -