CDSMOTE: class decomposition and synthetic minority class oversampling technique for imbalanced-data classification.
- Submitting institution
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Robert Gordon University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- Moreno-Garcia_2
- Type
- D - Journal article
- DOI
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10.1007/s00521-020-05130-z
- Title of journal
- Neural Computing and Applications
- Article number
- -
- First page
- 2839
- Volume
- 30 Jul
- Issue
- -
- ISSN
- 1433-3058
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2020
- URL
-
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- 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)
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- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The technique presented in this paper resulted from our findings in an industrial project, in collaboration with Det Norske Veritas (Brian.Bain@dnvgl.com - Oil & Gas certification company), funded by The Data Lab and the Oil and Gas Innovation Centre (https://www3.rgu.ac.uk/news/rgu-and-dnv-gl-join-forces-to-create-cost-saving-image-processing-software?), (https://www3.rgu.ac.uk/news/the-digital-oilfield-could-be-a-step-closer-as-new-technology-projects-get-support-from-ogic). This novel method for imbalanced data classification received added publicity following its application to the domain of medical image analysis, with funding from The Newton Fund and in partnership with UNAM Mexico (https://www.eveningexpress.co.uk/fp/news/local/robert-gordon-university-joins-forces-with-mexico-for-groundbreaking-study/).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -