Neighbourhood-based undersampling approach for handling imbalanced and overlapped data.
- Submitting institution
-
Robert Gordon University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- Elyan_2
- Type
- D - Journal article
- DOI
-
10.1016/j.ins.2019.08.062
- Title of journal
- Information Sciences
- Article number
- -
- First page
- 47
- Volume
- 509
- Issue
- -
- ISSN
- 1872-6291
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2019
- 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
-
-
- Research group(s)
-
-
- Citation count
- 17
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The novel method presented in this paper to detect negative instances in complex and non-linearly separable datasets significantly improved performance of supervised machine learning algorithms in follow on work applied to medical datasets - https://doi.org/10.1007/978-3-030-%2049186-4_30.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
