Open Access
Learning the parameters of a multiple criteria sorting method from large sets of assignment examples
TLDR
This study considers a sorting method in which categories are defined by profiles separating consecutive categories, that corresponds to a simplified version of ELECTRE Tri, and considers a learning procedure that relies on a set of known assignment examples to find parameters compatible with these assignments.About:
The article was published on 2013-01-01 and is currently open access. It has received 56 citations till now. The article focuses on the topics: Sorting.read more
Citations
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Book ChapterDOI
Learning a Majority Rule Model from Large Sets of Assignment Examples
TL;DR: A new metaheuristic designed to learn the parameters of an MR-Sort model that works in two phases that are iterated and reports the results of numerical tests, providing insights on the algorithm behavior.
Journal ArticleDOI
Specifics of medical data mining for diagnosis aid: A survey
TL;DR: This survey paper provides guidelines to contribute to the design of daily helpful diagnosis aid systems, by focusing on the specifics of diagnosis aid, and the related data modeling goals.
Journal ArticleDOI
Learning criteria weights of an optimistic Electre Tri sorting rule
TL;DR: This work proposes algorithms for eliciting the criteria weights and majority threshold in a version of the optimistic E lectre T ri rule, which raises additional difficulties w.r.t. the pessimistic rule, and describes an algorithm that computes robust alternatives assignments from assignment examples by solving mixed integer programs.
Journal ArticleDOI
On the relations between ELECTRE TRI-B and ELECTRE TRI-C and on a new variant of ELECTRE TRI-B
Denis Bouyssou,Thierry Marchant +1 more
TL;DR: It is shown that a variant of ELECTRE TRI that uses limiting profiles may have some advantages over the original method, and if an ordered partition obtained with one method can be obtained with the other method, after a suitable redefinition of the profiles.
Journal ArticleDOI
Learning monotone preferences using a majority rule sorting model
TL;DR: This work considers the problem of learning a function assigning objects into ordered categories and describes an algorithm designed for learning such a model on the basis of assignment examples, called MR-Sort, which competes with the other two methods, and leads to a model that is interpretable.
References
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Book
Aide multicritère à la décision : méthodes et cas
Bernard Roy,Denis Bouyssou +1 more
TL;DR: Theorem 1.1.3.2.
Journal ArticleDOI
Ordinal regression revisited: multiple criteria ranking using a set of additive value functions
TL;DR: Distinguishing necessary and possible consequences of preference information on the complete set of actions, UTAGMS answers questions of robustness analysis and can support the decision maker when his/her preference statements cannot be represented in terms of an additive value function.
Journal ArticleDOI
Inferring an ELECTRE TRI Model from Assignment Examples
Vincent Mousseau,Roman Słowiński +1 more
TL;DR: An interactive approach that infers the parameters of an ELECTRE TRI model from assignment examples is proposed that lies in the possibility given to the DM to revise his/her assignment examples and/or to give additional information before the optimization phase restarts.
Book
Evaluation and Decision Models with Multiple Criteria: Stepping Stones for the Analyst
TL;DR: This work presents a meta-modelling architecture suitable for multi-Dimensional preference models and shows clear trends in preference-based decision-making that have been identified in recent years.
Journal ArticleDOI
A user-oriented implementation of the ELECTRE-TRI method integrating preference elicitation support
TL;DR: This paper presents a new implementation of an existing method called ELECTRE TRI, which integrates specific functionalities supporting the decision maker (DM) in the preference elicitation process and aims at reducing the cognitive effort required from the DM in the phase of calibration of the preference model.