@revista_internacional{964, keywords = {Concept lattice, Heterogeneous information, Multi-attribute group decision-making, Linguistic truth-valued lattice implication algebra}, author = {Kuo Pang and Chao Fu and Luis Martínez and Jun Liu and Li Zou and Mingyu Lu}, title = {An extended multi-expert concept lattice-based heterogeneous multi-attribute group decision-making approach}, abstract = {In practical multi-attribute group decision-making (MAGDM) problems, it is common to utilize heterogeneous representation forms to express distinct preference information for different experts, primarily due to their diverse backgrounds. To address the problem of reducing information loss from the aggregation of experts opinions in heterogeneous MAGDM as well as improving the interpretability of the decision-making process, this paper introduces a concept lattice-based heterogeneous MAGDM approach. The heterogeneous multi-expert formal context is first proposed to capture the heterogeneous evaluation information of alternatives provided by different experts. Then, extended multi-expert concept lattices are constructed to aggregate evaluation information of alternatives by different experts. In this case, all concepts are considered to minimize information loss during the aggregation process. Second, to obtain more reasonable decision results, the distance between the concept intents and the heterogeneous positive and negative ideal solutions is considered, and the alternatives are ranked based on this measure. In addition, the MAGDM process for each alternative is visualized using the extended multi-expert concept lattices. This representation aids in identifying key concepts, their interdependencies, and the overall impact on the decision result. Finally, numerical examples and comparative analysis validate the validity and rationality of the proposed approach in heterogeneous MAGDM.}, year = {2024}, journal = {Information Sciences}, volume = {665}, pages = {120345}, issn = {0020-0255}, url = {https://www.sciencedirect.com/science/article/pii/S0020025524002585}, doi = {https://doi.org/10.1016/j.ins.2024.120345}, }