VisualUTA
Ordinal regression revisited: multiple criteria ranking with a set of additive value functions. VisualUTA is the first implementation of the UTA^GMS method for multiple criteria ranking of alternatives from set A using a set of additive value functions which result from an ordinal regression. The preference information provided by the decision maker is a set of pairwise comparisons on a subset of alternatives A^R, called reference alternatives. The preference model built via ordinal regression is a set of all additive value functions compatible with the preference information. Using this model, one can define two relations in the set A: the necessary weak preference relation (strong outranking) which holds for any two alternatives a, b from set A if and only if for all compatible value functions a is preferred to b, and the possible weak preference relation (weak outranking) which holds for this pair if and only if for at least one compatible value function a is preferred to b. These relations establish a necessary (strong) and a possible (weak) ranking of alternatives from A, being, respectively, a partial preorder and a strongly complete and negatively transitive relation. The UTA^GMS method is intended to be used interactively, with an increasing subset A^R and a progressive statement of pairwise comparisons. When no preference information is provided, the necessary weak preference relation is a weak dominance relation, and the possible weak preference relation is a complete relation. Every new pairwise comparison of reference alternatives is enriching the necessary relation and it is impoverishing the possible relation, so that they converge with the growth of the preference information. Moreover, the method can support the decision maker also when his/her preference statements cannot berepresented in terms of an additive value function.
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References in zbMATH (referenced in 109 articles , 1 standard article )
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Sorted by year (- Greco, Salvatore; Mousseau, Vincent; Słowiński, Roman: Multiple criteria sorting with a set of additive value functions (2010)
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- Roy, Bernard: Robustness in operational research and decision aiding: a multi-faceted issue (2010)
- Chen, Yen-Liang; Cheng, Li-Chen: Mining maximum consensus sequences from group ranking data (2009)
- Köksalan, Murat; Özpeynirci, Selin Bilgin: An interactive sorting method for additive utility functions (2009)
- Vetschera, Rudolf: Learning about preferences in electronic negotiations - A volume-based measurement method (2009)
- Waegeman, Willem; De Baets, Bernard; Boullart, Luc: Kernel-based learning methods for preference aggregation (2009)
- Greco, Salvatore; Mousseau, Vincent; Słowiński, Roman: Ordinal regression revisited: Multiple criteria ranking using a set of additive value functions (2008)