hh.sePublications
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
Mining data with random forests: A survey and results of new tests
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).ORCID iD: 0000-0003-2185-8973
Department of Electrical & Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
Department of Electrical & Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
2011 (English)In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 44, no 2, p. 330-349Article in journal (Refereed) Published
Abstract [en]

Random forests (RF) has become a popular technique for classification, prediction, studying variable importance, variable selection, and outlier detection. There are numerous application examples of RF in a variety of fields. Several large scale comparisons including RF have been performed. There are numerous articles, where variable importance evaluations based on the variable importance measures available from RF are used for data exploration and understanding. Apart from the literature survey in RF area, this paper also presents results of new tests regarding variable rankings based on RF variable importance measures. We studied experimentally the consistency and generality of such rankings. Results of the studies indicate that there is no evidence supporting the belief in generality of such rankings. A high variance of variable importance evaluations was observed in the case of small number of trees and small data sets.

Place, publisher, year, edition, pages
Oxford: Pergamon Press, 2011. Vol. 44, no 2, p. 330-349
Keywords [en]
Random forests, Variable importance, Variable selection, Classifier, Data proximity
National Category
Human Computer Interaction
Identifiers
URN: urn:nbn:se:hh:diva-5457DOI: 10.1016/j.patcog.2010.08.011ISI: 000284446200014Scopus ID: 2-s2.0-77958064179OAI: oai:DiVA.org:hh-5457DiVA, id: diva2:345742
Available from: 2010-08-26 Created: 2010-08-26 Last updated: 2018-01-12Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Verikas, Antanas

Search in DiVA

By author/editor
Verikas, Antanas
By organisation
Intelligent systems (IS-lab)
In the same journal
Pattern Recognition
Human Computer Interaction

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 327 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