Data-informed fuzzy measures for fuzzy integration of intervals and fuzzy numbers
- Submitting institution
-
University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1333787
- Type
- D - Journal article
- DOI
-
10.1109/TFUZZ.2014.2382133
- Title of journal
- IEEE Transactions on Fuzzy Systems
- Article number
- -
- First page
- 1861
- Volume
- 23
- Issue
- 5
- ISSN
- 1063-6706
- Open access status
- Out of scope for open access requirements
- Month of publication
- December
- Year of publication
- 2014
- 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
-
2
- Research group(s)
-
-
- Citation count
- 15
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Data fusion focusses on techniques to identify the most appropriate combination of multiple data sources into one overall assessment. This is vital in a vast number of areas, from sensor fusion to the aggregation of expert assessments in cyber-security. This paper introduces a novel approach that enables powerful data fusion in cases where nothing or little is known about the sources, e.g., we do not know which source knows best. The approach extracts relevant information directly from numeric, interval-valued and fuzzy number (i.e. distribution) valued evidence contributed by each source and thus enables comprehensively informed fusion.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
