mdh.sePublications
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
Building similarity metrics reflecting utility in case-based reasoning
Mälardalen University, Department of Computer Science and Electronics.ORCID iD: 0000-0001-9857-4317
Mälardalen University, Department of Computer Science and Electronics.ORCID iD: 0000-0002-5562-1424
2006 (English)In: Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, E-ISSN 1875-8967, Vol. 17, no 4, p. 407-416Article in journal (Refereed) Published
Abstract [en]

Fundamental to case-based reasoning is the idea that similar problems have similar solutions. The meaning of the concept of "similarity" can vary in different situations and remains an issue. Since we want to identify and retrieve truly useful or relevant cases for problem solving, the metrics of similarity must be defined suitably to reflect the utility of cases for solving a particular target problem. A framework for utility-oriented similarity modeling is developed in this paper. The main idea is to exploit a case library to obtain adequate samples of utility from pairs of cases. The task of similarity modeling then becomes the customization of the parameters in a similarity metric to minimize the discrepancy between the assessed similarity values and the utility scores desired. A new structure for similarity metrics is introduced which enables the encoding of single feature impacts and more competent approximation of case utility. Preliminary experimental results have shown that the proposed approach can be used for learning with a surprisingly small case base without the risk of over-fitting and that it yields stable system performance with variations in the threshold selected for case retrieval.

Place, publisher, year, edition, pages
2006. Vol. 17, no 4, p. 407-416
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-6895ISI: 000241929600008Scopus ID: 2-s2.0-33749332766OAI: oai:DiVA.org:mdh-6895DiVA, id: diva2:236905
Conference
Workshop of the Swedish-Artificial-Intelligence-Society/Swedish-Society-on-Learning-Systems, Lund, Sweden, 12-14 April, 2005
Available from: 2009-09-25 Created: 2009-09-25 Last updated: 2017-12-13Bibliographically approved

Open Access in DiVA

No full text in DiVA

Scopus

Authority records BETA

Xiong, NingFunk, Peter

Search in DiVA

By author/editor
Xiong, NingFunk, Peter
By organisation
Department of Computer Science and Electronics
In the same journal
Journal of Intelligent & Fuzzy Systems
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 36 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