A New Algorithm of Similarity Measuring for Multi-experts’ Qualitative Knowledge Based on Outranking Relations in Case-Based Reasoning Methodology

  • Hui Li
  • Xiang-Yang Li
  • Jie Gu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


Qualitative knowledge reasoning is a key content in knowledge science. Case-based reasoning is one of the main reasoning methodologies in artificial intelligence. Outranking relation methods, called ELECTRE and others, have been developed. In this research, a new algorithm of similarity measuring for qualitative problems in the presence of multiple experts based on outranking relations in case-based reasoning was proposed. Strict preference, weak preference, and indifference relations were introduced to formulate imprecision, uncertainty, incompleteness knowledge from multi-experts. Case similarities were integrated through aggregating house on the foundation of outranking relations. Experiments indicated that the new algorithm got accordant outcome with traditional quantitative similarity mode but extended its application range.


Actual Preference Similarity Algorithm Concordance Index Multiple Expert Indifference Relation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Liao, S.-H.: Expert System Methodologies and Applications – A Decade Review from 1995 to 2004. Expert Systems with Applications 28, 93–103 (2005)CrossRefGoogle Scholar
  2. Turban, E., Aronson, J.E.: Decision Support Systems and Intelligent Systems, 6th edn. Prentice International Hall, Hong Kong (2001)Google Scholar
  3. Schank, R.C.: Dynamic Memory: A Theory of Learning in Computers and People. Cambridge University Press, New York (1982)Google Scholar
  4. Schank, R.C., Leake, D.: Creativity and Learning in a Case-Based Explainer. Artificial Intelligence 40, 353–385 (1989)CrossRefGoogle Scholar
  5. Aamodt, A., Plaza, E.: Case-based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Artificial Intelligence Communications 7, 39–59 (1994)Google Scholar
  6. Allen, B.P.: Case-based Reasoning: Business Applications. Communications of the ACM 37, 40–42 (1994)CrossRefGoogle Scholar
  7. Hunt, J.: Evolutionary Case Based Design. In: Watson, I.D. (ed.) UK CBR 1995. LNCS, vol. 1020, pp. 17–31. Springer, Heidelberg (1995)Google Scholar
  8. Finnie, G., Sun, Z.-H.: R5 Model for Case-based Reasoning. Knowledge-Based Systems 16, 59–65 (2003)CrossRefGoogle Scholar
  9. Roy, B.: The Outranking Approach and the Foundations of ELECTRE Methods. Theory and Decision 31, 49–73 (1991)CrossRefMathSciNetGoogle Scholar
  10. Roy, B.: Problems and Methods with Multiple Objective Functions. Mathematical Programming 1, 239–266 (1971)MATHCrossRefMathSciNetGoogle Scholar
  11. Chen, Z.-X.: Improved Algorithms of ELECTRE-I for Production Order Evaluation. Group Technology & Production Modernization 22, 19–21 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hui Li
    • 1
  • Xiang-Yang Li
    • 1
  • Jie Gu
    • 2
  1. 1.Harbin Institute of Technology, HarbinSchool of ManagementChina
  2. 2.School of SoftwareTsinghua UniversityBeijingChina

Personalised recommendations