Exploiting Data Reliability and Fuzzy Clustering for Journal Ranking
- Submitting institution
-
The University of Huddersfield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 30
- Type
- D - Journal article
- DOI
-
10.1109/TFUZZ.2016.2612265
- Title of journal
- IEEE Transactions on Fuzzy Systems
- Article number
- -
- First page
- 1306
- Volume
- 25
- Issue
- 5
- ISSN
- 1063-6706
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2016
- 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
-
3
- Research group(s)
-
-
- Citation count
- 22
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in an ERA2010 A* journal, this output proposes a methodological framework on which to aggregate multiple indicators to produce more robust journal rankings, via fuzzy aggregation and fuzzy clustering. Follow on research projects include “Fuzzy Rule Learning and Its Application in Smart Grid” Fundamental Research Funds for the Central Universities, China (grant no. 2016MS118); and ”Automated Assessment of Corneal Nerve Tortuosity Based on Semantic Feature Extraction” National Natural Science Foundation of China (Grant No. 61906181), both led by co-author Pan Su. Both rely on the paper’s methodological framework which enables the learning of data-driven fuzzy systems.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
