Induction of accurate and interpretable fuzzy rules from preliminary crisp representation
- Submitting institution
-
The University of Huddersfield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 40
- Type
- D - Journal article
- DOI
-
10.1016/j.knosys.2018.02.003
- Title of journal
- Knowledge-Based Systems
- Article number
- -
- First page
- 152
- Volume
- 146
- Issue
- -
- ISSN
- 0950-7051
- Open access status
- Compliant
- Month of publication
- February
- 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
-
3
- Research group(s)
-
-
- Citation count
- 19
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in the Knowledge-based Systems journal, ranked Q1 by Scimago, this output’s novelty lies in supporting the explicit formulation of, and inference with, domain knowledge, gaining insights into complex problems and facilitating the explanation of their solutions. The output’s method was used in project proposals which led to several funded research projects, including co-author Chen’s Grow MedTech project “Automated Diagnosis of Dementia using Explainable Artificial Intelligence” awarded by Research England (No. POM000224), and a project led by co-author Pan Su called “Grading of Corneal Nerve Tortuosity Based on Machine Learning”, funded by the China Postdoctoral Science Foundation (No. 2016MS118).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -