Fuzzy Rule Based Interpolative Reasoning Supported by Attribute Ranking
- Submitting institution
-
Aberystwyth University / Prifysgol Aberystwyth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 28064132
- Type
- D - Journal article
- DOI
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10.1109/TFUZZ.2018.2812182
- Title of journal
- IEEE Transactions on Fuzzy Systems
- Article number
- -
- First page
- 2758
- Volume
- 26
- Issue
- 5
- ISSN
- 1063-6706
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2018
- 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
-
4
- Research group(s)
-
-
- Citation count
- 11
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Archiving version of the first approach to strengthening approximate reasoning with sparse knowledge, via introduction of a novel learning mechanism that generates data from the given knowledge base. IEEE TFS is one of the most highly regarded outlets across all areas of computer science and informatics (JCR-Clarivate). Earlier conference version (not returned) won best paper award at 16th Annual Workshop on Computational Intelligence. Led to invitation for Shen to present a keynote at IEEE 2020 World Congress on Computational Intelligence (largest and foremost, biannual international event on Computational Intelligence).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -