Skip to main content
SpringerLink
Log in
Book cover

Challenges in Computational Statistics and Data Mining pp 155–171Cite as

Dominance-Based Rough Set Approach to Multiple Criteria Ranking with Sorting-Specific Preference Information

  • Chapter
  • First Online:

Part of the book series: Studies in Computational Intelligence ((SCI,volume 605))

Abstract

A novel multiple criteria decision aiding method is proposed, that delivers a recommendation characteristic for ranking problems but employs preference information typical for sorting problems. The method belongs to the category of ordinal regression methods: it starts with preference information provided by the Decision Maker (DM) in terms of decision examples, and then builds a preference model that reproduces these exemplary decisions. The ordinal regression is analogous to inductive learning of a model that is true in the closed world of data where it comes from. The sorting examples show an assignment of some alternatives to pre-defined and ordered quality classes. Although this preference information is purely ordinal, the number of quality classes separating two assigned alternatives is meaningful for an ordinal intensity of preference. Using an adaptation of the Dominance-based Rough Set Approach (DRSA), the method builds from this information a decision rule preference model. This model is then applied on a considered set of alternatives to finally rank them from the best to the worst. The decision rule preference model resulting from DRSA is able to represent the preference information about the ordinal intensity of preference without converting this information into a cardinal scale. Moreover, the decision rules can be interpreted straightforwardly by the DM, facilitating her understanding of the feedback between the preference information and the preference model. An illustrative case study performed in this paper supports this claim.

This is a preview of subscription content, 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   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

References

  1. Bana e Costa CA, Vansnick J-C (1994) MACBETH: an interactive path towards the construction of cardinal value functions. Int Trans Oper Res 1(4):387–500

    Google Scholar 

  2. Błaszczyński J, Słowiński R, Szeląg M (2010) Probabilistic rough set approaches to ordinal classification with monotonicity constraints. In: Hüllermeier E, Kruse R, Hoffmann F (eds) IPMU 2010. Lecture notes in artificial intelligence, vol 6178. Springer, Berlin, pp 99–108

    Google Scholar 

  3. Błaszczyński J, Słowiński R, Szeląg M (2011) Sequential covering rule induction algorithm for variable consistency rough set approaches. Inf Sci 181:987–1002

    Google Scholar 

  4. Corrente S, Greco S, Kadziński M, Słowiński R (2013) Robust ordinal regression in preference learning and ranking. Mach Learn 93:381–422

    Article  MathSciNet  MATH  Google Scholar 

  5. Dembczyński K, Kotłowski W, Słowiński R, Szeląg M (2010) Learning of rule ensembles for multiple attribute ranking problems. In: Fürnkranz J, Hüllermeier E (eds) Preference learning. Springer, Berlin, pp 217–247

    Google Scholar 

  6. Doumpos M, Zopounidis C (2012) Preference disaggregation and statistical learning for multicriteria decision support: a review. Eur J Oper Res 209(3):203–214

    Article  MathSciNet  Google Scholar 

  7. Figueira J, Greco S, Słowiński R (2009) Building a set of additive value functions representing a reference preorder and intensities of preference: grip method. Eur J Oper Res 195(2):460–486

    Article  Google Scholar 

  8. Fortemps P, Greco S, Słowiński R (2008) Multicriteria decision support using rules that represent rough-graded preference relations. Eur J Oper Res 188(1):206–223

    Article  MATH  Google Scholar 

  9. Fürnkranz J, Hüllermeier E (2003) Pairwise preference learning and ranking. In: Lavrac N, Gamberger D, Todorovski L, Blockeel H (eds) Proceedings of the European conference on machine learning (ECML 2003). Lecture notes in artificial intelligence, vol 2837. Springer, pp 145–156

    Google Scholar 

  10. Fürnkranz J, Hüllermeier E (eds) (2010) Preference learning. Springer, Berlin

    Google Scholar 

  11. Greco S, Matarazzo B, Słowiński R (1999) Rough approximation of a preference relation by dominance relations. Eur J Oper Res 117:63–83

    Article  MATH  Google Scholar 

  12. Greco S, Matarazzo B, Słowiński R (2001) Rough sets theory for multicriteria decision analysis. Eur J Oper Res 129(1):1–47

    Article  Google Scholar 

  13. Greco S, Matarazzo B, Słowiński R (2005) Decision rule approach. In: Figueira J, Greco S, Ehrgott M (eds) Multiple criteria decision analysis: state of the art surveys. Chap. 13. Springer, New York, pp 507–562

    Google Scholar 

  14. Greco S, Matarazzo B, Słowiński R (2005) Preference representation by means of conjoint measurement and decision rule model. In: Bouyssou D, Jacquet-Lagrèze E, Perny P, Słowiński R, Vanderpooten D, Vincke P (eds) Aiding decisions with multiple criteria—essays in honor of Bernard Roy. Kluwer, Boston, pp 263–313

    Google Scholar 

  15. Greco S, Matarazzo B, Słowiński R, Stefanowski J (2001) An algorithm for induction of decision rules consistent with the dominance principle. In: Ziarko W, Yao YY (eds) Rough sets and current trends in computing 2001. Lecture notes in artificial intelligence, vol 2005. Springer, Berlin, pp 304–313

    Google Scholar 

  16. Grzymała-Busse JW (1992) LERS—a system for learning from examples based on rough sets. In: Słowiński R (ed) Intelligent decision support. Handbook of Applications and Advances of the Rough Sets Theory. Kluwer, Dordrecht, pp 3–18

    Google Scholar 

  17. Grzymała-Busse JW (1997) A new version of the rule induction system LERS. Fundamenta Informaticae 31(1):27–39

    MATH  Google Scholar 

  18. Liu T-Y (2011) Learning to rank for information retrieval. Springer, Berlin

    Book  MATH  Google Scholar 

  19. Roy B, Słowiński R (2013) Questions guiding the choice of a multicriteria decision aiding method. EURO J Decis Process 1(1):69–97

    Article  Google Scholar 

  20. Saaty T (1980) The analytic hierarchy process. McGraw Hill, New York

    MATH  Google Scholar 

  21. Słowiński R, Greco S, Matarazzo B (2009) Rough sets in decision making. In: Meyers RA (ed) Encyclopedia of complexity and systems science. Springer, New York, pp 7753–7786

    Google Scholar 

  22. Słowiński R, Greco R, Matarazzo B (2014) Rough set based decision support. In: Burke EK, Kendall G (eds) Search methodologies: introductory tutorials in optimization and decision support techniques, Chap. 19, 2nd edn. Springer, New York, pp 557–609

    Google Scholar 

  23. Stefanowski J (2001) Algorytmy indukcji reguł decyzyjnych w odkrywaniu wiedzy. Rozprawy, vol 361. Wydawnictwo Politechniki Poznańskiej

    Google Scholar 

Download references

Acknowledgments

The first author acknowledges financial support from the National Science Center (grant no. DEC-2013/11/D/ST6/03056). The third author declares that he is a scholarship holder within the 2012/2013 project “Scholarship support for Ph.D. students specializing in majors strategic for Wielkopolska’s development”, Sub-measure 8.2.2 of Human Capital Operational Programme, co-financed by European Union under the European Social Fund.

Author information

Authors and Affiliations

  1. Institute of Computing Science, Poznań University of Technology, 60-965, Poznań, Poland

    Miłosz Kadziński, Roman Słowiński & Marcin Szeląg

  2. Systems Research Institute, Polish Academy of Sciences, 01-447, Warsaw, Poland

    Roman Słowiński

Authors
  1. Miłosz Kadziński
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roman Słowiński
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marcin Szeląg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roman Słowiński .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada

    Stan Matwin

  2. Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland, and Warsaw University of Technology, Warsaw, Poland

    Jan Mielniczuk

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Kadziński, M., Słowiński, R., Szeląg, M. (2016). Dominance-Based Rough Set Approach to Multiple Criteria Ranking with Sorting-Specific Preference Information. In: Matwin, S., Mielniczuk, J. (eds) Challenges in Computational Statistics and Data Mining. Studies in Computational Intelligence, vol 605. Springer, Cham. https://doi.org/10.1007/978-3-319-18781-5_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-18781-5_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18780-8

  • Online ISBN: 978-3-319-18781-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation