A Compact Evolutionary Interval-Valued Fuzzy Rule-Based Classification System for the Modeling and Prediction of Real-World Financial Applications With Imbalanced Data
- Submitting institution
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The University of Essex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1108
- Type
- D - Journal article
- DOI
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10.1109/tfuzz.2014.2336263
- Title of journal
- IEEE Transactions on Fuzzy Systems
- Article number
- 4
- First page
- 973
- Volume
- 23
- Issue
- 4
- ISSN
- 1063-6706
- Open access status
- Out of scope for open access requirements
- Month of publication
- July
- 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
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4
- Research group(s)
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A - Artificial Intelligence (AI)
- Citation count
- 75
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This IEEE-TFS (the top fuzzy systems journal) paper presented a ground-breaking system for Explainable AI which can automatically extract from huge imbalanced data a small set of short simple IF-Then rules (high interpretability) to generate highly accurate models for real-world financial applications. The research has a deep academic impact on e.g. Predicting Corporate Investment (Hajek, Pardubice, Czech Republic), Modelling International Trade (Yu ,Zhejiang, China), Designing Granular classifiers (Antonelli, Pisa, Italy). It also had deep real-world impact, being applied by various international financial institutions to various important applications like fraud detection, credit risk assessment, anti-money laundering and wealth management.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -