Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Learning Decision Forest from Evidential Data: the Random Training Set Sampling Approach
Univ Jinan, CHI.
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies. Blekinge Inst Technol, Dept Creat Technol, Karlskrona, Sweden..ORCID iD: 0000-0001-5824-425X
Univ Jinan, CHI.
2017 (English)In: International Conference on Systems and Informatics, IEEE , 2017, p. 1423-1428Conference paper, Published paper (Refereed)
Abstract [en]

To learn decision trees from uncertain data modelled by mass functions, the random training set sampling approach for learning belief decision forests is proposed. Given an uncertain training set, a collection of simple belief decision trees are trained separately on each corresponding new set drawn by random sampling from the original one. Then the final prediction is made by majority voting. After discussing the selection of parameters for belief decision forests, experiments on Balance scale data are carried on for performance validation. Results show that with different kinds of uncertainty, the proposed method guarantees an obvious improvement in classification accuracy.

Place, publisher, year, edition, pages
IEEE , 2017. p. 1423-1428
Series
International Conference on Systems and Informatics, ISSN 2474-0217
Keywords [en]
Decision trees, Decision Forest, Learning algorithms
National Category
Information Systems
Identifiers
URN: urn:nbn:se:bth-16125ISI: 000427752100257ISBN: 978-1-5386-1107-4 OAI: oai:DiVA.org:bth-16125DiVA, id: diva2:1201513
Conference
4th International Conference on Systems and Informatics (ICSAI), Hangzhou,
Available from: 2018-04-26 Created: 2018-04-26 Last updated: 2018-05-24Bibliographically approved

Open Access in DiVA

No full text in DiVA

Authority records BETA

Sun, Bin

Search in DiVA

By author/editor
Sun, Bin
By organisation
Department of Creative Technologies
Information Systems

Search outside of DiVA

GoogleGoogle Scholar

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 1 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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