Labelling strategies for hierarchical multi-label classification techniques
- Submitting institution
-
University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1324156
- Type
- D - Journal article
- DOI
-
10.1016/j.patcog.2016.02.017
- Title of journal
- Pattern Recognition
- Article number
- -
- First page
- 170
- Volume
- 56
- Issue
- -
- ISSN
- 0031-3203
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- Year of publication
- 2016
- 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
-
1
- Research group(s)
-
-
- Citation count
- 22
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- In the field of hierarchical multi-label classification, most algorithms do not directly provide a class label for each sample, but a score representing the confidence in the classification, which is a well-known barrier to tackling real world applications. This paper provides practical labelling scheme strategies for hierarchical multi-label classifiers. The proposed methodology is necessary for most existing and new hierarchical multi-label techniques to provide final predictions.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
