Generalized multi-criteria majority-rule sorting for handling imprecise or missing evaluations
MEYER, Patrick
Lab-STICC_TB_CID_DECIDE
Département Logique des Usages, Sciences sociales et Sciences de l'Information [LUSSI]
Lab-STICC_TB_CID_DECIDE
Département Logique des Usages, Sciences sociales et Sciences de l'Information [LUSSI]
MEYER, Patrick
Lab-STICC_TB_CID_DECIDE
Département Logique des Usages, Sciences sociales et Sciences de l'Information [LUSSI]
< Reduce
Lab-STICC_TB_CID_DECIDE
Département Logique des Usages, Sciences sociales et Sciences de l'Information [LUSSI]
Language
en
Communication dans un congrès avec actes
This item was published in
Proceedings EWG MCDA 2015 : 82nd Meeting of the Euro Working Group on Multiple Criteria Decision Aiding, EWG MCDA 2015 : 82nd Meeting of the Euro Working Group on Multiple Criteria Decision Aiding, 2015-09-24, Odense. 2015p. .
English Abstract
We propose an extension of a multi-criteria majority-rule sorting model that allows the handling of problems where the decision alternatives contain imprecise or even missing evaluations. Due to the imprecise nature of the ...Read more >
We propose an extension of a multi-criteria majority-rule sorting model that allows the handling of problems where the decision alternatives contain imprecise or even missing evaluations. Due to the imprecise nature of the evaluations we offer the possibility of assigning an alternative to one or more neighboring categories, both as input for inferring the model parameters as well as the output of the classification. Our contribution also contains an algorithmic approach for extracting the parameters of this model during an elicitation process, which is validated across a wide range of generated datasets.Read less <
English Keywords
MCDA
Sorting methods
Missing data
Outranking methods
Origin
Hal imported