Handling uncertainty in citizen science data: towards an improved amateur-based large-scale classification
- Submitting institution
-
University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1395097
- Type
- D - Journal article
- DOI
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10.1016/j.ins.2018.12.011
- Title of journal
- Information Sciences
- Article number
- -
- First page
- 301
- Volume
- 479
- Issue
- -
- ISSN
- 0020-0255
- Open access status
- Compliant
- Month of publication
- December
- 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)
-
-
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Citizen science is emerging as a powerful tool to label datasets on a large scale. However, involving amateurs in the classification of scientific data introduces bias and uncertainty that prevents the research community from trusting Citizen Science outcomes. This paper provides a novel fuzzy-based methodology for citizen science data to handle uncertainty and improve the quality and confidence in the results. The research was conducted in a real-world problem, galaxy morphology classification, demonstrating both its effectiveness and the need for this type of approach to maximise the benefits of Citizen Science.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
