Change search
CiteExportLink to record
Permanent link

Direct link
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
Evaluation of a Variance-Based Nonconformity Measure for Regression Forests
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2016 (English)In: Conformal and Probabilistic Prediction with Applications: 5th International Symposium, COPA 2016, Madrid, Spain, April 20-22, 2016, Proceedings / [ed] Alexander Gammerman, Zhiyuan Luo, Jesús Vega, Vladimir Vovk, Springer, 2016, 75-89 p.Conference paper, Published paper (Refereed)
Abstract [en]

In a previous large-scale empirical evaluation of conformal regression approaches, random forests using out-of-bag instances for calibration together with a k-nearest neighbor-based nonconformity measure, was shown to obtain state-of-the-art performance with respect to efficiency, i.e., average size of prediction regions. However, the use of the nearest-neighbor procedure not only requires that all training data have to be retained in conjunction with the underlying model, but also that a significant computational overhead is incurred, during both training and testing. In this study, a more straightforward nonconformity measure is investigated, where the difficulty estimate employed for normalization is based on the variance of the predictions made by the trees in a forest. A large-scale empirical evaluation is presented, showing that both the nearest-neighbor-based and the variance-based measures significantly outperform a standard (non-normalized) nonconformity measure, while no significant difference in efficiency between the two normalized approaches is observed. Moreover, the evaluation shows that state-of-the-art performance is achieved by the variance-based measure at a computational cost that is several orders of magnitude lower than when employing the nearest-neighbor-based nonconformity measure.

Place, publisher, year, edition, pages
Springer, 2016. 75-89 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9653
Keyword [en]
Conformal prediction, Nonconformity measures, Regression, Random forests
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-137476DOI: 10.1007/978-3-319-33395-3_6ISBN: 978-3-319-33394-6 (print)ISBN: 978-3-319-33395-3 (print)OAI: oai:DiVA.org:su-137476DiVA: diva2:1062751
Conference
5th International Symposium, COPA 2016, Madrid, Spain, April 20-22, 2016
Available from: 2017-01-08 Created: 2017-01-08 Last updated: 2017-01-11Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Boström, Henrik
By organisation
Department of Computer and Systems Sciences
Information Systems

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 5 hits
CiteExportLink to record
Permanent link

Direct link
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