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Learning fuzzy rules for similarity assessment in case-based reasoning
Mälardalen University, School of Innovation, Design and Engineering.ORCID iD: 0000-0001-9857-4317
2011 (English)In: Expert systems with applications, ISSN 0957-4174, Vol. 38, no 9, 10780-10786 p.Article in journal (Refereed) Published
Abstract [en]

Fundamental to case-based reasoning is the assumption that similar problems have similar solutions. The meaning of the concept of "similarity" can vary in different situations and remains an issue. This paper proposes a novel similarity model consisting of fuzzy rules to represent the semantics and evaluation criteria for similarity. We believe that fuzzy if-then rules present a more powerful and flexible means to capture domain knowledge for utility oriented similarity modeling than traditional similarity measures based on feature weighting. Fuzzy rule-based reasoning is utilized as a case matching mechanism to determine whether and to which extent a known case in the case library is similar to a given problem in query. Further, we explain that such fuzzy rules for similarity assessment can be learned from the case library using genetic algorithms. The key to this is pair-wise comparisons of cases with known solutions in the case library such that sufficient training samples can be derived for genetic-based fuzzy rule learning. The evaluations conducted have shown the superiority of the proposed method in similarity modeling over traditional schemes as well as the feasibility of learning fuzzy similarity rules from a rather small case base while still yielding competent system performance.

Place, publisher, year, edition, pages
2011. Vol. 38, no 9, 10780-10786 p.
Identifiers
URN: urn:nbn:se:mdh:diva-13611DOI: 10.1016/j.eswa.2011.01.151ISI: 000291118500007Scopus ID: 2-s2.0-79955620798OAI: oai:DiVA.org:mdh-13611DiVA: diva2:466154
Available from: 2011-12-15 Created: 2011-12-15 Last updated: 2013-12-03Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
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Language
  • de-DE
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  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
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