@revista_internacional{925,
  keywords = {Concept lattice, Linguistic truth-valued lattice implication algebra, Linguistic information processing, Multi-attribute group decision-making},
  author = {Kuo Pang and Luis Martínez and Nan Li and Jun Liu and Li Zou and Mingyu Lu},
  title = {A concept lattice-based expert opinion aggregation method for multi-attribute group decision-making with linguistic information},
  abstract = {During the multi-attribute group decision-making (MAGDM) processing, the individuals often hold different opinions about the alternatives. It is necessary to aggregate the different individual opinions into a unified group opinion. In the real world, experts sometimes use linguistic expressions to evaluate attributes in uncertain environments. To address the problem of reducing the information loss of expert opinion aggregation in MAGDM, this paper proposes a MAGDM approach based on linguistic concept lattices in the context of uncertain linguistic expression. A linguistic concept lattice for multi-expert linguistic formal context is first constructed based on linguistic truth-valued lattice implication algebra, which can express both comparable and incomparable linguistic information in the decision-making process. Different expert opinions are aggregated via the extent of fuzzy linguistic concepts, which can reduce information loss in the aggregation process. Second, meet-irreducible elements in the linguistic concept lattice are introduced to reduce the computational complexity of obtaining all fuzzy linguistic concepts in the decision-making process. the distance between the intents of different fuzzy linguistic concepts is considered to enhance the rationality of linguistic decision results. In addition, the expert’s decision-making process for each alternative is visualized via linguistic concept lattices. Finally, the case study and comparative analysis illustrate the validity and rationality of the proposed approach in MAGDM with linguistic information.},
  year = {2024},
  journal = {Expert Systems with Applications},
  volume = {237},
  pages = {121485},
  issn = {0957-4174},
  url = {https://www.sciencedirect.com/science/article/pii/S0957417423019875},
  doi = {https://doi.org/10.1016/j.eswa.2023.121485},
}