Skip to main content
SpringerLink
Log in

Learning a Majority Rule Model from Large Sets of Assignment Examples

  • Conference paper
Algorithmic Decision Theory (ADT 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8176))

Included in the following conference series:

Abstract

Learning the parameters of a Majority Rule Sorting model (MR-Sort) through linear programming requires to use binary variables. In the context of preference learning where large sets of alternatives and numerous attributes are involved, such an approach is not an option in view of the large computing times implied. Therefore, we propose a new metaheuristic designed to learn the parameters of an MR-Sort model. This algorithm works in two phases that are iterated. The first one consists in solving a linear program determining the weights and the majority threshold, assuming a given set of profiles. The second phase runs a metaheuristic which determines profiles for a fixed set of weights and a majority threshold. The presentation focuses on the metaheuristic and reports the results of numerical tests, providing insights on the algorithm behavior.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Yu, W.: Aide multicritère la décision dans le cadre de la problématique du tri: méthodes et applications. PhD thesis, LAMSADE, Université Paris Dauphine, Paris (1992)

    Google Scholar 

  2. Roy, B., Bouyssou, D.: Aide multicritère la décision: méthodes et cas. Economica Paris (1993)

    Google Scholar 

  3. Bouyssou, D., Marchant, T.: An axiomatic approach to noncompensatory sorting methods in MCDM, I: The case of two categories. European Journal of Operational Research 178(1), 217–245 (2007)

    Article  MATH  Google Scholar 

  4. Bouyssou, D., Marchant, T.: An axiomatic approach to noncompensatory sorting methods in MCDM, II: More than two categories. European Journal of Operational Research 178(1), 246–276 (2007)

    Article  MATH  Google Scholar 

  5. Mousseau, V., Slowinski, R.: Inferring an ELECTRE TRI model from assignment examples. Journal of Global Optimization 12(1), 157–174 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  6. Mousseau, V., Figueira, J., Naux, J.P.: Using assignment examples to infer weights for ELECTRE TRI method: Some experimental results. European Journal of Operational Research 130(1), 263–275 (2001)

    Article  MATH  Google Scholar 

  7. Ngo The, A., Mousseau, V.: Using assignment examples to infer category limits for the ELECTRE TRI method. Journal of Multi-criteria Decision Analysis 11(1), 29–43 (2002)

    Article  MATH  Google Scholar 

  8. Dias, L., Mousseau, V., Figueira, J., Clímaco, J.: An aggregation/disaggregation approach to obtain robust conclusions with ELECTRE TRI. European Journal of Operational Research 138(1), 332–348 (2002)

    Article  MATH  Google Scholar 

  9. Dias, L., Mousseau, V.: Inferring Electre’s veto-related parameters from outranking examples. European Journal of Operational Research 170(1), 172–191 (2006)

    Article  MATH  Google Scholar 

  10. Doumpos, M., Marinakis, Y., Marinaki, M., Zopounidis, C.: An evolutionary approach to construction of outranking models for multicriteria classification: The case of the ELECTRE TRI method. European Journal of Operational Research 199(2), 496–505 (2009)

    Article  MATH  Google Scholar 

  11. Leroy, A., Mousseau, V., Pirlot, M.: Learning the parameters of a multiple criteria sorting method. In: Brafman, R. (ed.) ADT 2011. LNCS, vol. 6992, pp. 219–233. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  12. Cailloux, O., Meyer, P., Mousseau, V.: Eliciting ELECTRE TRI category limits for a group of decision makers. European Journal of Operational Research 223(1), 133–140 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  13. Fürnkranz, J., Hüllermeier, E.: Preference learning: An introduction. In: Fürnkranz, J., Hüllermeier, E. (eds.) Preference Learning, pp. 1–17. Springer (2010)

    Google Scholar 

  14. Bouyssou, D., Pirlot, M.: A characterization of concordance relations. European Journal of Operational Research 167/2, 427–443 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  15. Bouyssou, D., Pirlot, M.: Further results on concordance relations. European Journal of Operational Research 181, 505–514 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  16. Pirlot, M.: General local search methods. European Journal of Operational Research 92(3), 493–511 (1996)

    Article  MATH  Google Scholar 

  17. Butler, J., Jia, J., Dyer, J.: Simulation techniques for the sensitivity analysis of multi-criteria decision models. European Journal of Operational Research 103(3), 531–546 (1997)

    Article  MATH  Google Scholar 

  18. Devaud, J., Groussaud, G., Jacquet-Lagrèze, E.: UTADIS: Une méthode de construction de fonctions d’utilité additives rendant compte de jugements globaux. In: European Working Group on MCDA, Bochum, Germany (1980)

    Google Scholar 

  19. Doumpos, M., Zopounidis, C.: Multicriteria Decision Aid Classification Methods. Kluwer Academic Publishers, Dordrecht (2002)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. École Centrale Paris, Grande Voie des Vignes, 92295, Châtenay Malabry, France

    Olivier Sobrie & Vincent Mousseau

  2. Faculté Polytechnique, Université de Mons, 9, rue de Houdain, 7000, Mons, Belgique

    Olivier Sobrie & Marc Pirlot

Authors
  1. Olivier Sobrie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vincent Mousseau
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marc Pirlot
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. LIP 6, UPMC, 75005, Paris, France

    Patrice Perny

  2. UMONS, Faculty of Engineering, Mathematics and Operations Research, Université de Mons, 9, Rue de Houdain, 7000, Mons, Belgium

    Marc Pirlot

  3. CNRS, LAMSADE, Université Paris Dauphine, Place du Maréchal de Lattre de Tassigny, 75016, Paris, France

    Alexis Tsoukiàs

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sobrie, O., Mousseau, V., Pirlot, M. (2013). Learning a Majority Rule Model from Large Sets of Assignment Examples. In: Perny, P., Pirlot, M., Tsoukiàs, A. (eds) Algorithmic Decision Theory. ADT 2013. Lecture Notes in Computer Science(), vol 8176. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41575-3_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-41575-3_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41574-6

  • Online ISBN: 978-3-642-41575-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation