A Note on the Detection of Outliers in a Binary Outranking Relation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10173)


We address the problem of outliers detection in a binary outranking relation. These elements are supposed to be rare, dissimilar to the majority of other elements and are likely to influence the outcomes of the considered method. We propose a model based on the distance introduced by De Smet and Montano and extend it to different samplings of the set of alternatives (which are used as a comparison basis). This leads to study the distribution of distance values. The presence of outliers is detected by the identification of bi-modal distributions. We illustrate this on examples based on the Human Development Index, the Environmental Performance Index (where artificial outliers are added) and the Shanghai Ranking of World Universities.


Human Development Index Concordance Index Environmental Performance Index Multiple Outlier Shanghai Ranking 
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.


  1. 1.
    Behzadian, M., Kazemzadh, R.B., Albadvi, A., Aghdasi, D.: PROMETHEE: a comprehensive literature review on methodologies and applications. Eur. J. Oper. Res. 200(1), 198–215 (2010)CrossRefzbMATHGoogle Scholar
  2. 2.
    Brans, J.P., De Smet, Y.: Promethee methods. In: Figueira, J., Greco, S., Egrgott, M. (eds.) Multiple Criteria Decision Analysis: State of the Art Surveys, 2nd edn, pp. 187–220. Springer, Boston (2016)CrossRefGoogle Scholar
  3. 3.
    De Smet, Y., Eppe, S.: Multicriteria relational clustering: the case of binary outranking matrices. In: Ehrgott, M., Fonseca, C.M., Gandibleux, X., Hao, J.-K., Sevaux, M. (eds.) EMO 2009. LNCS, vol. 5467, pp. 380–392. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-01020-0_31 CrossRefGoogle Scholar
  4. 4.
    De Smet, Y., Montano Guzman, L.: Towards multicriteria clustering: an extension of the k-means algorithm. Eur. J. Oper. Res. 158(2), 390–398 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Doan, N.A.V., Milojevic, D., Robert, F., De Smet, Y.: A MOO-based methodology for designing 3D-stacked integrated circuits. J. Multi-Criteria Decis. Anal. 21(1–2), 43–63 (2013)Google Scholar
  6. 6.
    Dyer, J.S.: MAUT multiattribute utility theory. In: Figueira, J., Greco, S., Egrgott, M. (eds.) Multiple Criteria Decision Analysis: State of the Art Surveys, pp. 265–292. Springer, Boston (2005)CrossRefGoogle Scholar
  7. 7.
    Figueira, J., Mousseau, V., Roy, B.: ELECTRE methods. In: Figueira, J., Greco, S., Ergott, M. (eds.) Multiple Criteria Decision Analysis: State of the Art Surveys, pp. 133–162. Springer, Boston (2005)CrossRefGoogle Scholar
  8. 8.
    Hites, R., De Smet, Y., Risse, N., Salazar-Neumann, M., Vincke, P.: About the applicability of MCDA to some robustness problems. Eur. J. Oper. Res. 174(1), 322–332 (2006)CrossRefzbMATHGoogle Scholar
  9. 9.
    Rosseeuw, P.J., Leroy, A.L.: Robust Regression and Outlier Detection. Wiley Series in Probability and Mathematical Statistics. Wiley, Hoboken (1987)CrossRefGoogle Scholar
  10. 10.
    Sarrazin, R., De Smet, Y.: Design safer and greener road projects by using a multi-objective optimization evolutionary approach. Int. J. Multicriteria Decis. Mak. 61, 14–33 (2016)CrossRefGoogle Scholar
  11. 11.
    Vincke, P.: Multicriteria Decision-Aid. Wiley, New York (1992)zbMATHGoogle Scholar
  12. 12.
    Zopounidis, C., Doumpos, M.: Multiple Criteria Decision Making: Applications in Management and Engineering. Springer, Heidelberg (2017, to appear)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.CoDE Department, Ecole Polytechnique de BruxellesUniversité libre de BruxellesBrusselsBelgium

Personalised recommendations