Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Bagging likelihood-based belief decision trees
University of Jinan, CHI.
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.ORCID-id: 0000-0001-5824-425X
University of Science and Technology of China, CHI.
2017 (engelsk)Inngår i: 20th International Conference on Information Fusion, Fusion 2017: Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2017, s. 321-326, artikkel-id 8009664Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

To embed ensemble techniques into belief decision trees for performance improvement, the bagging algorithm is explored. Simple belief decision trees based on entropy intervals extracted from evidential likelihood are constructed as the base classifiers, and a combination of individual trees promises to lead to a better classification accuracy. Requiring no extra querying cost, bagging belief decision trees can obtain good classification performance by simple belief tree combination, making it an alternative to single belief tree with querying. Experiments on UCI datasets verify the effectiveness of bagging approach. In various uncertain cases, the bagging method outperforms single belief tree without querying, and is comparable in accuracy to single tree with querying. © 2017 International Society of Information Fusion (ISIF).

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2017. s. 321-326, artikkel-id 8009664
Emneord [en]
bagging, belief function theory, decision trees, evidential likelihood, Decision theory, Forestry, Information fusion, Uncertainty analysis, Bagging algorithms, Bagging approach, Classification accuracy, Classification performance, Ensemble techniques, Trees (mathematics)
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-15216DOI: 10.23919/ICIF.2017.8009664ISI: 000410938300047Scopus ID: 2-s2.0-85029439190ISBN: 9780996452700 (tryckt)OAI: oai:DiVA.org:bth-15216DiVA, id: diva2:1145526
Konferanse
20th International Conference on Information Fusion, Fusion, Xian
Tilgjengelig fra: 2017-09-29 Laget: 2017-09-29 Sist oppdatert: 2018-01-13bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstScopus

Personposter BETA

Sun, Bin

Søk i DiVA

Av forfatter/redaktør
Sun, Bin
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric

doi
isbn
urn-nbn
Totalt: 101 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf