MRPR: A MapReduce solution for prototype reduction in big data classification
- Submitting institution
-
University of Newcastle upon Tyne
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 213327-176193-1292
- Type
- D - Journal article
- DOI
-
10.1016/j.neucom.2014.04.078
- Title of journal
- Neurocomputing
- Article number
- -
- First page
- 331
- Volume
- 150
- Issue
- PA
- ISSN
- 0925-2312
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2014
- URL
-
http://dx.doi.org/10.1016/j.neucom.2014.04.078
- 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
-
4
- Research group(s)
-
B - Interdisciplinary Computing and Complex Biosystems (ICOS)
- Citation count
- 130
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The vast amount of data that all aspects of science and society are able to generate nowadays require new paradigms for their processing and analysis, collectively known as big data technologies. This output presents a Hadoop-based prototype generation method, generating a reduced set of synthetic training examples representative of an original, large, dataset. The trade-off between efficiency and solution quality is thoroughly explored with varying degrees of parallelism. The paper provides an efficient tool for big data analytics and advice on how to adjust it to different scenarios.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -