Högskolan i Skövde

his.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • apa-cv
  • 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
Inconsistency: Friend or Foe
The School of Business and Informatics, University of Borås, Sweden.
The School of Business and Informatics, University of Borås, Sweden.
Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Skövde Cognition and Artificial Intelligence Lab (SCAI))
2007 (Engelska)Ingår i: The 2007 International Joint Conferenceon Neural Networks: IJCNN 2007 Conference Proceedings, IEEE, 2007, s. 1383-1388Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

One way of obtaining accurate yet comprehensible models is to extract rules from opaque predictive models. When evaluating rule extraction algorithms, one frequently used criterion is consistency; i.e. the algorithm must produce similar rules every time it is applied to the same problem. Rule extraction algorithms based on evolutionary algorithms are, however, inherently inconsistent, something that is regarded as their main drawback. In this paper, we argue that consistency is an overvalued criterion, and that inconsistency can even be beneficial in some situations. The study contains two experiments, both using publicly available data sets, where rules are extracted from neural network ensembles. In the first experiment, it is shown that it is normally possible to extract several different rule sets from an opaque model, all having high and similar accuracy. The implication is that consistency in that perspective is useless; why should one specific rule set be considered superior? Clearly, it should instead be regarded as an advantage to obtain several accurate and comprehensible descriptions of the relationship. In the second experiment, rule extraction is used for probability estimation. More specifically, an ensemble of extracted trees is used in order to obtain probability estimates. Here, it is exactly the inconsistency of the rule extraction algorithm that makes the suggested approach possible.

Ort, förlag, år, upplaga, sidor
IEEE, 2007. s. 1383-1388
Serie
Proceedings of the International Joint Conference on Neural Networks, ISSN 2161-4393, E-ISSN 2161-4407
Nationell ämneskategori
Datavetenskap (datalogi) Systemvetenskap, informationssystem och informatik
Forskningsämne
Teknik
Identifikatorer
URN: urn:nbn:se:his:diva-2104DOI: 10.1109/IJCNN.2007.4371160ISI: 000254291101059Scopus ID: 2-s2.0-51749099818ISBN: 978-1-4244-1380-5 (digital)ISBN: 1-4244-1380-X (tryckt)ISBN: 978-1-4244-1379-9 (tryckt)OAI: oai:DiVA.org:his-2104DiVA, id: diva2:32380
Konferens
The 2007 International Joint Conference on Neural Networks, IJCNN 2007, August 12-17, 2007, Renaissance Orlando Resort, Florida, USA
Tillgänglig från: 2008-05-30 Skapad: 2008-05-30 Senast uppdaterad: 2021-04-22Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltextScopus

Person

Niklasson, Lars

Sök vidare i DiVA

Av författaren/redaktören
Niklasson, Lars
Av organisationen
Institutionen för kommunikation och informationForskningscentrum för Informationsteknologi
Datavetenskap (datalogi)Systemvetenskap, informationssystem och informatik

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetricpoäng

doi
isbn
urn-nbn
Totalt: 528 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • apa-cv
  • 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