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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)

Abstract

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.

Keywords

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.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

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

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