Research on linguistic multi-attribute decision making method for normal cloud similarity

Changlin Xu; Li Yang

doi:10.1016/j.heliyon.2023.e20961

Research on linguistic multi-attribute decision making method for normal cloud similarity

Heliyon. 2023 Oct 13;9(10):e20961. doi: 10.1016/j.heliyon.2023.e20961. eCollection 2023 Oct.

Authors

Changlin Xu^{1

2}, Li Yang¹

Affiliations

¹ School of Mathematics and Information Science, North Minzu University, Yinchuan 750021, China.
² The Key Laboratory of Intelligent Information and Big Data Processing of Ning Xia Province, Yinchuan, 750021, China.

Abstract

Aiming at the uncertainty linguistic transformation problem in multi-attribute decision making, a decision-making method based on normal cloud similarity was proposed. Firstly, starting from the normal cloud characteristic curves, a normal cloud similarity measurement method based on Wasserstein distance is proposed by combing with the normal cloud entropy-containing expectation curve, which is using the Wasserstein distance to characterize the similarity characteristics of probability distribution. The properties of the proposed similarity measure are discussed in the paper. Secondly, the performance of the proposed method is compared and analyzed with the existed methods by numerical simulation experiment and time series data classification experiment. The experimental results show that the proposed method has good similarity discrimination ability, high classification accuracy and low CPU time cost. Finally, the method was successfully applied into linguistic multi-attribute decision making, and TOPSIS thought is used to compare and rank the schemes, so as to realize the final decision.

Keywords: Entropy-containing expectation curve; Multi-attribute decision making; Normal cloud model; Similarity measure; TOPSIS method; Wasserstein distance.