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Fuzzy Rule-Based Similarity Model Enables Learning from Small Case Bases
Mälardalen University, School of Innovation, Design and Engineering. (IS)ORCID iD: 0000-0001-9857-4317
2013 (English)In: Applied Soft Computing, ISSN 1568-4946, Vol. 13, no 4, 2057-2064 p.Article in journal (Refereed) Published
Abstract [en]

The concept of similarity plays a fundamental role in case-based reasoning. However, the meaning of “similarity” can vary in situations and is largely domain dependent. This paper proposes a novel similarity model consisting of linguistic fuzzy rules as the knowledge container. We believe that fuzzy rules representation offers a more flexible means to express the knowledge and criteria for similarity assessment than traditional similarity metrics. The learning of fuzzy similarity rules is performed by exploiting the case base, which is utilized as a valuable resource with hidden knowledge for similarity learning. A sample of similarity is created from a pair of known cases in which the vicinity of case solutions reveals the similarity of case problems. We do pair-wise comparisons of cases in the case base to derive adequate training examples for learning fuzzy similarity rules. The empirical studies have demonstrated that the proposed approach is capable of discovering fuzzy similarity knowledge from a rather low number of cases, giving rise to the competence of CBR systems to work on a small case library.

Place, publisher, year, edition, pages
2013. Vol. 13, no 4, 2057-2064 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-17472DOI: 10.1016/j.asoc.2012.11.009ISI: 000316767100043Scopus ID: 2-s2.0-84890431809OAI: oai:DiVA.org:mdh-17472DiVA: diva2:579803
Available from: 2012-12-20 Created: 2012-12-20 Last updated: 2014-06-16Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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