Advertisement

Clustering Alternatives and Learning Preferences Based on Decision Attitudes and Weighted Overlap Dominance

  • Camilo Franco
  • Jens Leth Hougaard
  • Kurt Nielsen
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 671)

Abstract

An initial assessment on a given set of alternatives is necessary for understanding complex decision problems and their possible solutions. Attitudes and preferences articulate and come together under a decision process that should be explicitly modeled for understanding and solving the inherent conflict of decision making. This paper revises multi-criteria modeling of imprecise data, inferring outranking and indifference binary relations and classifying alternatives according to their similarity or dependency. After the initial assessment on the set of alternatives, preference orders are built according to the attitudes of decision makers, aiding the decision process by identifying solutions with minimal dissention.

Keywords

Decision attitudes Dependency-based clustering Preference learning Consensus and dissention 

Notes

Acknowledgements

This research has been partially supported by the Danish Industry Foundation and the Center for research in the Foundations of Electronic Markets (CFEM), funded by the Danish Council for Strategic Research.

References

  1. 1.
    Bustince H, Barrenechea E, Pagola M, Fernandez J.: The notions of overlap and grouping functions. Studies in Fuzziness and Soft Computing 336,137-156 (2016)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bustince H, Montero J, Pagola M, Barrenechea E, Gómez D.: A survey of interval-valued fuzzy sets. In: Pedrycz W, Skowron A, Kreinovich V (eds.), Handbook of Granular Computing, Chichester: John Wiley & Sons; 2008, p. 491-515.Google Scholar
  3. 3.
    Fernández JM, Murakami S.: Extending Yager’s orness concept for the OWA aggregators to other mean operators. Fuzzy Sets and Systems 139, 515-542 (2003)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Fodor J, Roubens M.: Fuzzy Preference Modelling and Multicriteria Decision Support. Kluwer Academic Publishers, Dordrecht (1994)CrossRefMATHGoogle Scholar
  5. 5.
    Franco C, Hougaard JL, Nielsen K.: A fuzzy approach to the Weighted Overlap Dominance Model. Proceedings of the 15th Conference of the Spanish Association for Artificial Intelligence, Madrid, Spain, September 17-20, 2013, p. 1240-1249.Google Scholar
  6. 6.
    Franco C, Hougaard JL, Nielsen K.: Ranking alternatives based on imprecise multi-criteria data and pairwise overlap dominance relations. MSAP Working Papers Series (2014)Google Scholar
  7. 7.
    Franco C, Hougaard JL, Nielsen K.: Handling risk attitudes for preference learning and intelligent decision support. Lecture Notes in Computer Science 9321, 78-89 (2015)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Grabisch M, Labreuche Ch.: A decade of application of the Choquet and Sugeno integrals in multi-criteria decision aid. Annals of Operations Research 175, 247-290 (2010)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Grattan-Guinness I.: Fuzzy membership mapped onto interval and many-valued quantities. Mathematical Logic Quarterly 22, 149-60 (1976)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Hougaard JL, Nielsen K.: Weighted Overlap Dominance - A procedure for interactive selection on multidimensional interval data. Applied Mathematical Modelling 35, 3958-3969 (2011)CrossRefMATHGoogle Scholar
  11. 11.
    Y. Narukawa, V. Torra. Fuzzy measures and Choquet integral on discrete spaces. In: B. Reusch (ed.), Computational Intelligence, Theory and Applications, Berlin: Springer; 2004, p. 573-581.Google Scholar
  12. 12.
    Roy B.: The outranking approach and the foundation of ELECTRE methods. Theory and Decision 31, 49-73 (1991)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Camilo Franco
    • 1
  • Jens Leth Hougaard
    • 1
  • Kurt Nielsen
    • 1
  1. 1.IFRO, Faculty of ScienceCopenhagen UniversityFrederiksbergDenmark

Personalised recommendations