Multiobjective programming for type-2 hierarchical fuzzy inference trees
- Submitting institution
-
The University of Reading
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 78950
- Type
- D - Journal article
- DOI
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10.1109/TFUZZ.2017.2698399
- Title of journal
- IEEE Transactions on Fuzzy Systems
- Article number
- -
- First page
- 915
- Volume
- 26
- Issue
- 2
- ISSN
- 1063-6706
- Open access status
- Compliant
- Month of publication
- -
- 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
-
2
- Research group(s)
-
9 - DSAI
- Citation count
- 8
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The significance of this paper is that it presents a novel general-purpose supervised learning algorithm for regression and classification tasks. The paper combines fuzzy systems with genetic programming to generate an efficient lightweight interpretable model as opposed to neural networks. This research work impacts theoretical studies in machine learning, and demonstrably has wide-ranging application including in time-series prediction and pharmaceutical drug dissolution rate prediction.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -