Missing data imputation using fuzzy-rough methods
- Submitting institution
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Aberystwyth University / Prifysgol Aberystwyth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9632064
- Type
- D - Journal article
- DOI
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10.1016/j.neucom.2016.04.015
- Title of journal
- Neurocomputing
- Article number
- -
- First page
- 152
- Volume
- 205
- Issue
- -
- ISSN
- 0925-2312
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2016
- URL
-
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- Supplementary information
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- Request cross-referral to
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- 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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1
- Research group(s)
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-
- Citation count
- 34
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- First application of fuzzy-rough set theory to missing value imputation. The work was since extended internationally leading researchers in the area (e.g., by M. Hong et al at University of Minnesota 2015; Li et al. at Southwest Jiaotong University, 2019). It has led to significant further developments for fuzzy-based methods for: incomplete data (Liu et al., 2017), a study of R packages for missing value imputation (Yadav et al., 2018), classification (Zahin et al., 2018), and improved imputation methods (Cheng et al., 2019; Garcia et al., 2019; Samat et al., 2016; Vluymans 2019).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -