Evidence combination based on credal belief redistribution for pattern classification
- Submitting institution
-
Oxford Brookes University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 186970376
- Type
- D - Journal article
- DOI
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10.1109/TFUZZ.2019.2911915
- Title of journal
- IEEE Transactions on Fuzzy Systems
- Article number
- -
- First page
- 618
- Volume
- 28
- Issue
- 4
- ISSN
- 1063-6706
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2019
- 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
- 41
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This recent paper has already had a strong impact in the field of ensemble classification. Classifier fusion using belief functions has been studied before. The originality of this work is the proposal to redistribute the evidence provided by the test data according to its similarity with the training examples, leading to a more robust approach which outperforms competitors across the board on one of the UCI dataset repository. This led to its publication in a top impact factor journal, and ongoing discussions with Prof Osman from the Fraunhofer Institute on joint EU projects on multimodal classification and an Erasmus+ exchange.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -