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

doi:10.1109/ISKE.2017.8258801

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

Department of Computer Science

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

3 Citations (Scopus)

353 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 language	English
Title of host publication	2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2017)
Subtitle of host publication	Proceedings of a meeting held 24-26 November 2017, Nanjing, China
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	9
ISBN (Electronic)	9781538618295
ISBN (Print)	9781538618301
DOIs	https://doi.org/10.1109/ISKE.2017.8258801
Publication status	Published - Feb 2018
Event	The 12th International Conference on Intelligent Systems and Knowledge Engineering - Nanjing, China Duration: 24 Nov 2017 → 26 Nov 2017 http://iske2017.njupt.edu.cn/index

Conference

Conference	The 12th International Conference on Intelligent Systems and Knowledge Engineering
Abbreviated title	ISKE 2017
Country/Territory	China
City	Nanjing
Period	24/11/17 → 26/11/17
Internet address	http://iske2017.njupt.edu.cn/index

Keywords

Semi-Supervised
Consensus Model
Linguistic-Natured Information

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10.1109/ISKE.2017.8258801

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Palomares Carrascosa, I., Sellak, H., Ouhbi, B., & Frikh, B. (2018). Adaptive semi-supervised consensus model for multi-criteria large group decision making in a linguistic setting. In 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2017): Proceedings of a meeting held 24-26 November 2017, Nanjing, China [142] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ISKE.2017.8258801

Palomares Carrascosa, Ivan ; Sellak, Hamza ; Ouhbi, Brahim et al. / Adaptive semi-supervised consensus model for multi-criteria large group decision making in a linguistic setting. 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2017): Proceedings of a meeting held 24-26 November 2017, Nanjing, China. Institute of Electrical and Electronics Engineers (IEEE), 2018.

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

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{\textquoteright} 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.",

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Palomares Carrascosa, I, Sellak, H, Ouhbi, B & Frikh, B 2018, Adaptive semi-supervised consensus model for multi-criteria large group decision making in a linguistic setting. in 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2017): Proceedings of a meeting held 24-26 November 2017, Nanjing, China., 142, Institute of Electrical and Electronics Engineers (IEEE), The 12th International Conference on Intelligent Systems and Knowledge Engineering, Nanjing, China, 24/11/17. https://doi.org/10.1109/ISKE.2017.8258801

Adaptive semi-supervised consensus model for multi-criteria large group decision making in a linguistic setting. / Palomares Carrascosa, Ivan; Sellak, Hamza; Ouhbi, Brahim et al.

2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2017): Proceedings of a meeting held 24-26 November 2017, Nanjing, China. Institute of Electrical and Electronics Engineers (IEEE), 2018. 142.

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

TY - GEN

T1 - Adaptive semi-supervised consensus model for multi-criteria large group decision making in a linguistic setting

AU - Palomares Carrascosa, Ivan

AU - Sellak, Hamza

AU - Ouhbi, Brahim

AU - Frikh, Bouchra

PY - 2018/2

Y1 - 2018/2

N2 - 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.

AB - 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.

KW - Semi-Supervised

KW - Consensus Model

KW - Linguistic-Natured Information

U2 - 10.1109/ISKE.2017.8258801

DO - 10.1109/ISKE.2017.8258801

M3 - Conference Contribution (Conference Proceeding)

SN - 9781538618301

BT - 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2017)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - The 12th International Conference on Intelligent Systems and Knowledge Engineering

Y2 - 24 November 2017 through 26 November 2017

ER -

Palomares Carrascosa I, Sellak H, Ouhbi B, Frikh B. Adaptive semi-supervised consensus model for multi-criteria large group decision making in a linguistic setting. In 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2017): Proceedings of a meeting held 24-26 November 2017, Nanjing, China. Institute of Electrical and Electronics Engineers (IEEE). 2018. 142 https://doi.org/10.1109/ISKE.2017.8258801

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

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