• français
    • English
  • English 
    • français
    • English
  • Login
JavaScript is disabled for your browser. Some features of this site may not work without it.
BIRD Home

Browse

This CollectionBy Issue DateAuthorsTitlesSubjectsJournals BIRDResearch centres & CollectionsBy Issue DateAuthorsTitlesSubjectsJournals

My Account

Login

Statistics

View Usage Statistics

Dominance Based Monte Carlo algorithm for preference elicitation in the multi-criteria sorting problem: Some performance tests

Thumbnail
Date
2017
Dewey
Programmation, logiciels, organisation des données
Sujet
Dominance Based Monte Carlo algorithm
DOI
http://dx.doi.org/10.1007/978-3-319-67504-6_4
Book title
Algorithmic Decision Theory 5th International Conference, ADT 2017, Luxembourg, Luxembourg, October 25–27, 2017, Proceedings
Author
Jörg Rothe
Publisher
Springer
Publisher city
Berlin Heidelberg
Year
2017
ISBN
978-3-319-67503-9
Book URL
10.1007/978-3-319-67504-6
URI
https://basepub.dauphine.fr/handle/123456789/18972
Collections
  • LAMSADE : Publications
Metadata
Show full item record
Author
Denat, Tom
Ozturk, Meltem
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Type
Communication / Conférence
Item number of pages
50-64
Abstract (EN)
In this article, we study the Dominance Based Monte Carlo algorithm, a model-free Multi-Criteria Decision Aiding (MCDA) method for sorting problems, which was first proposed in Denat and Öztürk (2016). The sorting problem consists in assigning each object to a category, both the set of objects and the set of categories being predefined. This method is based on a sub-set of objects which are assigned to categories by a decision maker and aims at being able to assign the remaining objects to categories according to the decision makers preferences. This method is said model-free, which means that we do not assume that the decision maker’s reasoning follows some well-known and explicitly described rules or logic system. It is assumed that monotonicity should be respected as well as the learning set. The specificity of this approach is to be stochastic. A Monte Carlo principle is used where the median operator aggregates the results of independent and randomized experiments. In a previous article some theoretical properties that are met by this method were studied. Here we want to assess its performance through a k-fold validation procedure and compare this performance to those of other preference elicitation algorithms. We also show how the result of this method converges to a deterministic value when the number of trials or the size of the learning set increases.

  • Accueil Bibliothèque
  • Site de l'Université Paris-Dauphine
  • Contact
SCD Paris Dauphine - Place du Maréchal de Lattre de Tassigny 75775 Paris Cedex 16

 Content on this site is licensed under a Creative Commons 2.0 France (CC BY-NC-ND 2.0) license.