Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence
Abstract
Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. To provide a comprehensive perspective on the development of distributed linguistic representations in decision making, we present the taxonomy of existing distributed linguistic representations. Then, we review the key elements of distributed linguistic information processing in decision making, including the distance measurement, aggregation methods, distributed linguistic preference relations, and distributed linguistic multiple attribute decision making models. Next, we provide a discussion on ongoing challenges and future research directions from the perspective of data science and explainable artificial intelligence.
Cite
@article{arxiv.2008.01499,
title = {Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence},
author = {Yuzhu Wu and Zhen Zhang and Gang Kou and Hengjie Zhang and Xiangrui Chao and Cong-Cong Li and Yucheng Dong and Francisco Herrera},
journal= {arXiv preprint arXiv:2008.01499},
year = {2020}
}
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37 pages