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 |
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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 | |
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 |
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Abbreviated title | ISKE 2017 |
Country/Territory | China |
City | Nanjing |
Period | 24/11/17 → 26/11/17 |
Internet address |
Keywords
- Semi-Supervised
- Consensus Model
- Linguistic-Natured Information