Handling the Multiplicity of Solutions in a Moea Based PDA-THESEUS Framework for Multi-Criteria Sorting

Covantes, Edgar; Fernández, Eduardo; Navarro, Jorge

doi:10.1515/fcds-2016-0013

Cited by 14 publications

(3 citation statements)

References 23 publications

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“…-The out-of-sample effectiveness does not improve with the number of limiting profiles; this result differs from the one obtained in [9] for ELECTRE TRI-nB. It seems that a single "well-designed" limiting profile is enough to reach acceptable effectiveness, and more profiles do not increase the learnability of the model, at least within the analyzed range of training examples.…”

Section: ) Interclass-nbcontrasting

confidence: 59%

“…But such an indirect parameter elicitation becomes a very complex optimization problem when veto thresholds should be inferred; this is because inferring all the parameters simultaneously requires solving non-linear optimization problems with nonconvex constraints [8]. In such cases, evolutionary algorithms should be used as in [7], [9], [10]. These works have found that the non-linearity of the problem together with complex constraints are usually better handled by evolutionary algorithms than other exhaustive and/or metaheuristic approaches.…”

Section: Introductionmentioning

confidence: 99%

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Inferring Preferences for Multi-Criteria Ordinal Classification Methods Using Evolutionary Algorithms

Fernández¹,

Navarro

Solares³

et al. 2023

IEEE Access

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Multicriteria sorting involves assigning the objects of decisions (actions) into a priori known ordered classes considering the preferences of a decision maker (DM). Two new multicriteria sorting methods were recently proposed by the authors. These methods are based on a novel approach called interval-based outranking which provides the methods with attractive practical and theoretical characteristics. However, as is well known, defining parameter values for methods based on the outranking approach is often very difficult. This difficulty arises not only from the large number of parameters and the DM's lack of familiarity with them, but also from imperfectly known (even missing) information. Here, we address: i) how to elicit the parameter values of the two new methods, and ii) how to incorporate imperfect knowledge during the elicitation. We follow the preference disaggregation paradigm and use evolutionary algorithms to address it. Our proposal performs very well in a wide range of computational experiments. Interesting findings are: i) the method restores the assignment examples with high effectiveness using only three profiles in each limiting boundary or representative actions per class; and ii) the ability to appropriately assign unknown actions can be greatly improved by increasing the number of limiting profiles. INDEX TERMSEvolutionary algorithms; imperfect information, multiple criteria analysis, multiple criteria ordinal classification, outranking methods.

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Section: ) Interclass-nbcontrasting

confidence: 59%

Section: Introductionmentioning

confidence: 99%

Inferring Preferences for Multi-Criteria Ordinal Classification Methods Using Evolutionary Algorithms

Fernández¹,

Navarro

Solares³

et al. 2023

IEEE Access

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show abstract

“…On the one hand, the direct eliciting method has been criticized by Marchant [4] and Pirlot [5] arguing that the only valid preference input information is that arising from the DM's preference judgments about actions or pairs of actions. As stated by Covantes et al [6] and Doumpos et al [7], these criticisms are even more significant in the frame of outranking methods, since the DM must set parameters that are very unfamiliar to her/him (e.g., veto thresholds). On the other hand, indirect elicitation methods use regression-inspired techniques for inferring the model's parameters from a set of decision examples [6,7].…”

Section: Introductionmentioning

confidence: 99%

Robustness Analysis of an Outranking Model Parameters’ Elicitation Method in the Presence of Noisy Examples

Rangel-Valdez

Fernández

Cruz-Reyes

et al. 2018

Mathematical Problems in Engineering

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One of the main concerns in Multicriteria Decision Aid (MCDA) is robustness analysis. Some of the most important approaches to model decision maker preferences are based on fuzzy outranking models whose parameters (e.g., weights and veto thresholds) must be elicited. The so-called preference-disaggregation analysis (PDA) has been successfully carried out by means of metaheuristics, but this kind of works lacks a robustness analysis. Based on the above, the present research studies the robustness of a PDA metaheuristic method to estimate model parameters of an outranking-based relational system of preferences. The method is considered robust if the solutions obtained in the presence of noise can maintain the same performance in predicting preference judgments in a new reference set. The research shows experimental evidence that the PDA method keeps the same performance in situations with up to 10% of noise level, making it robust.

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Preference Disaggregation for Multicriteria Decision Aiding: An Overview and Perspectives

Doumpos

Zopounidis

2019

Multiple Criteria Decision Making

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Handling the Multiplicity of Solutions in a Moea Based PDA-THESEUS Framework for Multi-Criteria Sorting

Cited by 14 publications

References 23 publications

Inferring Preferences for Multi-Criteria Ordinal Classification Methods Using Evolutionary Algorithms

Inferring Preferences for Multi-Criteria Ordinal Classification Methods Using Evolutionary Algorithms

Robustness Analysis of an Outranking Model Parameters’ Elicitation Method in the Presence of Noisy Examples

Preference Disaggregation for Multicriteria Decision Aiding: An Overview and Perspectives

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