Deep-FS: a feature selection algorithm for deep Boltzmann machines
- Submitting institution
-
Nottingham Trent University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 12 - 697378
- Type
- D - Journal article
- DOI
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10.1016/j.neucom.2018.09.040
- Title of journal
- Neurocomputing
- Article number
- -
- First page
- 22
- Volume
- 322
- Issue
- -
- ISSN
- 0925-2312
- Open access status
- Compliant
- Month of publication
- September
- 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)
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A - Computing and Informatics Research Centre
- Citation count
- 12
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents, Deep-FS, to address the urgent need for algorithms which can optimally search through large datasets to remove redundant features. This paper demonstrated that Deep-FS can efficiently reduce the number of features in various types of data (image, biomedical, natural language and multi-modal data). The work forms part of our CIRC Leverhulme project grant. The significance of this paper is that it led to a collaboration with Laser Optical Ltd, where Deep-FS was applied to remove features from big multi-modal data used for evaluating laser welds. Further, Deep-FS was presented in keynote speeches at IEEE conferences.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
