Multiobjective sparse ensemble learning by means of evolutionary algorithms
- Submitting institution
-
De Montfort University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11155
- Type
- D - Journal article
- DOI
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10.1016/j.dss.2018.05.003
- Title of journal
- Decision Support Systems
- Article number
- -
- First page
- 86
- Volume
- 111
- Issue
- -
- ISSN
- 0167-9236
- Open access status
- Compliant
- Month of publication
- May
- 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
-
6
- Research group(s)
-
-
- Citation count
- 15
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The proposed novel formulation allows finding optimal sparse ensemble classifiers using evolutionary multiobjective algorithms. Moreover, it selects the most suitable ensemble classifier for a given dataset. The proposed approach outperforms significantly compressed sensing and two pruning ensemble learning methods on the well-known MNIST datasets and real-world remote sensing image change detection problem.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -