Adaptive semi-supervised consensus model for multi-criteria large group decision making in a linguistic setting

Ivan Palomares Carrascosa, Hamza Sellak, Brahim Ouhbi, Bouchra Frikh

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

3 Citations (Scopus)
267 Downloads (Pure)

Abstract

In this paper we investigate the consensus reaching problem for Large Group Multi-Criteria Decision Making (MCLGDM). We present an adaptive, semi-supervised consensus model for MCLGDM problems with preferences expressed as Comparative Linguistic Expressions. Specifically, our work introduces an adaptive, semi-supervised feedback mechanism that, depending on the positions of decision makers’ preferences and their level of uncertainty caused by hesitancy, requests human supervision to modify their preferences or updates them automatically. The proposed consensus model effectively handles large amounts of linguistic-natured information in consensus processes involving large groups. The methodology is illustrated and experimentally validated through a MCLGDM problem for candidate assessment in recruiting processes. Likewise, a theoretical comparison with similar works is provided.
Original languageEnglish
Title of host publication2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2017)
Subtitle of host publicationProceedings of a meeting held 24-26 November 2017, Nanjing, China
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages9
ISBN (Electronic)9781538618295
ISBN (Print)9781538618301
DOIs
Publication statusPublished - Feb 2018
EventThe 12th International Conference on Intelligent Systems and Knowledge Engineering - Nanjing, China
Duration: 24 Nov 201726 Nov 2017
http://iske2017.njupt.edu.cn/index

Conference

ConferenceThe 12th International Conference on Intelligent Systems and Knowledge Engineering
Abbreviated titleISKE 2017
CountryChina
CityNanjing
Period24/11/1726/11/17
Internet address

Keywords

  • Semi-Supervised
  • Consensus Model
  • Linguistic-Natured Information

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