Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Estimating Class Probabilities in Random Forests
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
2007 (Engelska)Ingår i: Proceedings of the Sixth International Conference on Machine Learning and Applications, IEEE , 2007, 211-216 s.Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

For both single probability estimation trees (PETs) and ensembles of such trees, commonly employed class probability estimates correct the observed relative class frequencies in each leaf to avoid anomalies caused by small sample sizes. The effect of such corrections in random forests of PETs is investigated, and the use of the relative class frequency is compared to using two corrected estimates, the Laplace estimate and the m-estimate. An experiment with 34 datasets from the UCI repository shows that estimating class probabilities using relative class frequency clearly outperforms both using the Laplace estimate and the m-estimate with respect to accuracy, area under the ROC curve (AUC) and Brier score. Hence, in contrast to what is commonly employed for PETs and ensembles of PETs, these results strongly suggest that a non-corrected probability estimate should be used in random forests of PETs. The experiment further shows that learning random forests of PETs using relative class frequency significantly outperforms learning random forests of classification trees (i.e., trees for which only an unweighted vote on the most probable class is counted) with respect to both accuracy and AUC, but that the latter is clearly ahead of the former with respect to Brier score.

Ort, förlag, år, upplaga, sidor
IEEE , 2007. 211-216 s.
Nationell ämneskategori
Systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-37838DOI: 10.1109/ICMLA.2007.64ISBN: 978-0-7695-3069-7 (tryckt)OAI: oai:DiVA.org:su-37838DiVA: diva2:305369
Konferens
Machine Learning and Applications, 2007. ICMLA 2007. Sixth International Conference on 13-15 Dec. 2007
Tillgänglig från: 2010-03-23 Skapad: 2010-03-23 Senast uppdaterad: 2011-06-28Bibliografiskt granskad

Open Access i DiVA

fulltext(134 kB)1137 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 134 kBChecksumma SHA-512
f417824e009e82a78c4933899363dc0202b6f1e9c60a98bcc7d2d16dfb39341e65e83a85f0feea0c5ff00a85ad1e815a4dd237f30388aa7876a7e5c777acfc70
Typ fulltextMimetyp application/pdf

Övriga länkar

Förlagets fulltext

Sök vidare i DiVA

Av författaren/redaktören
Boström, Henrik
Av organisationen
Institutionen för data- och systemvetenskap
Systemvetenskap

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 1137 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

Altmetricpoäng

Totalt: 75 träffar
RefereraExporteraLänk till posten
Permanent länk

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