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2020-11-06 - Colloque/Présentation - communication orale - Anglais - page(s)

Pirlot Marc , "An overview of multiple criteria sorting methods" in DA2PL 2020 From multiple criteria decision aid to preference learning , Trento, Italie, 2020

  • Codes CREF : Intelligence artificielle (DI1180), Modèles mathématiques d'aide à la décision (DI1151)
  • Unités de recherche UMONS : Mathématique et Recherche opérationnelle (F151)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech)
  • Centres UMONS : Centre de Recherche en Technologie de l’Information (CRTI)
Texte intégral :

Abstract(s) :

(Anglais) An overview of multiple criteria sorting methods Recent years have seen a blossoming of papers related to multiple criteria sorting methods, especially in the framework of the outranking approach. Stimulated by the boom of data mining and machine learning, this literature has developed different research themes within the field of multiple criteria decision analysis such as ⦁ designing algorithms for learning multicriteria sorting (or clustering) models from assignment examples; ⦁ designing procedures for eliciting the parameters of such methods by questioning the decision maker; ⦁ elaborating more complex or, on the contrary, simpler variants of previously proposed methods; ⦁ characterizing sorting methods by identifying the underlying models. These contributions showcase how a branch of decision analysis has drawn closer to machine learning, data analysis, AI and social choice, while maintaining specificities of decision theory as for instance, attachment to models and methods, small data rather than large data, lack of ground truth... The talk will survey the recent literature on multiple criteria sorting and clustering, trying to analyze how some themes develop within this community while they echo the methods followed in other domains.