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MapReduce distributed highly random fuzzy forest for noisy big data
Mälardalen University.
University of Granada, Spain.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-9857-4317
University of Granada, Spain.
2018 (English)In: 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery 2017 ICNC-FSKD-2017, 2018, p. 560-567Conference paper, Published paper (Refereed)
Abstract [en]

Nowadays the amounts of data available to us have the ever larger growth trend. On the other hand such data often contain noise. We call them noisy Big Data. There is an increasing need for learning methods that can handle such noisy Big Data for classification tasks. In this paper we propose a highly random fuzzy forest algorithm for learning an ensemble of fuzzy decision trees from a big data set contaminated with attribute noise. We also present the distributed version of the proposed learning algorithm implemented in the MapReduce framework. Experiment results have demonstrated that the proposed algorithm is faster and more accurate than the state-of-the-art approach particularly in the presence of attribute noise. 

Place, publisher, year, edition, pages
2018. p. 560-567
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-37074DOI: 10.1109/FSKD.2017.8393331ISI: 000437355300089Scopus ID: 2-s2.0-85050191333ISBN: 978-1-5386-2165-3 (print)OAI: oai:DiVA.org:mdh-37074DiVA, id: diva2:1153817
Conference
13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery 2017 ICNC-FSKD-2017, 29 Jul 2017, Guilin, China
Projects
ADAPTER: Adaptive Learning and Information Fusion for Online Classification Based on Evolving Big Data StreamsAvailable from: 2017-10-31 Created: 2017-10-31 Last updated: 2018-08-16Bibliographically approved

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Xiong, Ning

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf