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
Reliable Confidence Predictions Using Conformal Prediction
University of Borås, Faculty of Librarianship, Information, Education and IT. (CSL@BS)
University of Borås, Faculty of Librarianship, Information, Education and IT. (CSL@BS)
University of Borås, Faculty of Librarianship, Information, Education and IT. (CSL@BS)
2016 (Swedish)In: Lecture Notes in Computer Science, 2016, Vol. 9651, 77-88 p.Conference paper, Published paper (Refereed)
Abstract [en]

Conformal classiers output condence prediction regions, i.e., multi-valued predictions that are guaranteed to contain the true output value of each test pattern with some predened probability. In order to fully utilize the predictions provided by a conformal classier, it is essential that those predictions are reliable, i.e., that a user is able to assess the quality of the predictions made. Although conformal classiers are statistically valid by default, the error probability of the prediction regions output are dependent on their size in such a way that smaller, and thus potentially more interesting, predictions are more likely to be incorrect. This paper proposes, and evaluates, a method for producing rened error probability estimates of prediction regions, that takes their size into account. The end result is a binary conformal condence predictor that is able to provide accurate error probability estimates for those prediction regions containing only a single class label.

Place, publisher, year, edition, pages
2016. Vol. 9651, 77-88 p.
National Category
Computer Science
Research subject
Bussiness and IT
Identifiers
URN: urn:nbn:se:hb:diva-11963OAI: oai:DiVA.org:hb-11963DiVA: diva2:1077961
Conference
PAKDD 2016: Advances in Knowledge Discovery and Data Mining, Auckland, April 19-22, 2016
Available from: 2017-03-01 Created: 2017-03-01 Last updated: 2017-05-02Bibliographically approved

Open Access in DiVA

fulltext(475 kB)59 downloads
File information
File name FULLTEXT01.pdfFile size 475 kBChecksum SHA-512
d02506b75b73a68be3497fd3d61bd32535eef07655606d446a637e7c3854abcf9ed7cb723e897bf80e51dcc4f3377349a06f99793df4f5929ac0cf3f41409b53
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Linusson, HenrikJohansson, UlfLöfström, Tuwe
By organisation
Faculty of Librarianship, Information, Education and IT
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 59 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 339 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