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
Rule Extraction with Guaranteed Fidelity
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
University of Borås, School of Business and IT. (CSL@BS)
Show others and affiliations
2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper extends the conformal prediction framework to rule extraction, making it possible to extract interpretable models from opaque models in a setting where either the infidelity or the error rate is bounded by a predefined significance level. Experimental results on 27 publicly available data sets show that all three setups evaluated produced valid and rather efficient conformal predictors. The implication is that augmenting rule extraction with conformal prediction allows extraction of models where test set errors or test sets infidelities are guaranteed to be lower than a chosen acceptable level. Clearly this is beneficial for both typical rule extraction scenarios, i.e., either when the purpose is to explain an existing opaque model, or when it is to build a predictive model that must be interpretable.

Place, publisher, year, edition, pages
Springer , 2014.
Series
IFIP Advances in Information and Communication Technology, ISSN 1868-4238 ; 437
Keyword [en]
Rule extraction, Conformal Prediction, Decision trees, Machine learning, Data mining
National Category
Computer Sciences Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hb:diva-7322DOI: 10.1007/978-3-662-44722-2_30Local ID: 2320/14625ISBN: 978-3-662-44721-5 (print)ISBN: 978-3-662-44722-2 (print)OAI: oai:DiVA.org:hb-7322DiVA: diva2:888035
Conference
Artificial Intelligence Applications and Innovations
Note

Sponsorship:

This work was supported by the Swedish Foundation for Strategic Research through

the project High-Performance Data Mining for Drug Effect Detection (IIS11-0053)

and the Knowledge Foundation through the project Big Data Analytics by Online

Ensemble Learning (20120192).

Available from: 2015-12-22 Created: 2015-12-22 Last updated: 2018-01-10

Open Access in DiVA

fulltext(190 kB)146 downloads
File information
File name FULLTEXT01.pdfFile size 190 kBChecksum SHA-512
51d6c83aa752b8747446c1ce4b6f47e051db7114a3d4499a55e507591d8e6988738e3efe641669df60322745dd8c6323d47e1f127b680829f305755d584ab779
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Johansson, UlfKönig, RikardLinusson, HenrikLöfström, TuveBoström, Henrik
By organisation
School of Business and IT
Computer SciencesComputer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 146 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

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 174 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