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Article


Title

Learning the parameters of an outranking-based sorting model with characteristic class profiles from large sets of assignment examples

Authors

[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee | [ S ] student

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2022

Published in

Applied Soft Computing

Journal year: 2022 | Journal volume: vol. 116

Article type

scientific article

Publication language

english

Keywords
EN
  • multiple criteria decision aiding
  • multiple criteria sorting
  • preference learning
  • characteristic class profiles
  • metaheuristics
  • evolutionary computation
Abstract

EN We address the problem of learning the parameters of the outranking-based multiple criteria sorting model from large sets of assignment examples. We focus on a recently devised method called Electre TRI-rC, incorporating a single characteristic profile to describe each decision class. We introduce four algorithms aimed at the problem. They use different optimization techniques, including an evolutionary algorithm, linear programming combined with a genetic approach, simulated annealing, and a dedicated heuristic. We present the results of the experiments carried out on both artificial and real-world data sets. They reveal an impact of the comparison and veto thresholds, various sorting rules, and ensembles on the classification accuracy of the proposed algorithms. From a broader perspective, we contribute to cross-fertilizing the fields of Multiple Criteria Decision Aiding and Machine Learning for supporting real-world decision-making.

Date of online publication

20.12.2021

Pages (from - to)

108312-1 - 108312-19

DOI

10.1016/j.asoc.2021.108312

URL

https://www.sciencedirect.com/science/article/abs/pii/S1568494621011108

Comments

Article Number: 108312

Points of MNiSW / journal

200.0

Impact Factor

6.725 [List 2020]

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