A Novel Variable Precision Reduction Approach to Comprehensive Knowledge Systems
- Submitting institution
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Queen's University of Belfast
- Unit of assessment
- 12 - Engineering
- Output identifier
- 124343407
- Type
- D - Journal article
- DOI
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10.1109/TCYB.2017.2648824
- Title of journal
- IEEE Transactions on Cybernetics
- Article number
- -
- First page
- 661
- Volume
- 48
- Issue
- 2
- ISSN
- 2168-2267
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2017
- URL
-
-
- Supplementary information
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-
- 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
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4
- Research group(s)
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C - Electrical and Electronic
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Rough set theory is a relatively new, but an increasingly popular tool for information processing in the fields of machine learning, knowledge discovery, and data mining, particularly when dealing with uncertain information systems. This paper presents a pioneering approach to complete knowledge extraction from such systems, which, in contrast to competing approaches, is deterministic and comprehensive and scales well computationally. The work led to a follow-on NSF China grant (No. 61751205, RMB2.2million, 2018-2020) on ‘Multi-source heterogeneous data feature extraction and knowledge discovery’ and successful practical applications including Multi-factor Analysis of Violent Crime Information Systems, DOI: 10.1080/17517575.2014.986216.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -