Application of Complex Choquet Fuzzy Integral Classification Model in Scientific Decision Making

Dai, Xuezhen; Yaqin, Qiao; Nan, Jiangping

doi:10.1109/icicta51737.2020.00022

Cited by 2 publications

(1 citation statement)

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“…Specifically, problems in the decision-making of multiple criteria have been resolved using fuzzy sets (FSs) and intuitionistic fuzzy sets (IFSs) [7,8]. Xuezhen et al performed decision-making with the help of the Choquet integral [9]. However, fuzzy data still seem challenging if they are considered with IFS structures.…”

Section: Introductionmentioning

confidence: 99%

Analysis on the Hesitation and its Application to Decision Making

Yang,

Lee,

Kim

et al. 2024

Decis. Mak. Appl. Manag. Eng.

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A novel score function based on the Poincaré metric is proposed and applied to a decision-making problem. Decision-making on Fuzzy Sets (FSs) has been considered due to the flexibility of the data, and it is applied to the decision-making. However, decisions with FSs are sometimes nondecisive even for different membership degrees. Hence, Intuitionistic Fuzzy Sets (IFSs) data is applied to design a score function for the decision-making with the Poincaré metric. This function is supported by the profound information of IFSs; IFSs include hesitation degree together with membership and non-membership degree. Hence, IFS membership and non-membership degree are expressed as two-dimensional vectors satisfying the Poincaré metric for simplification. At the same time, the proposed approach addresses the hesitation information in the IFS data. Next, a score function is proposed, constructed and provided. The proposed score function has a strict monotonic property and addresses the preference without resorting to the accuracy function. The strict monotonic property guarantees the preference of all attributes. Additionally, the existing problem of score function design in IFSs is addressed: they return zero scores even with different meanings for the same membership and non-membership degree. The advantages of the proposed score function over existing ones are demonstrated through illustrative examples. From the calculation results, the proposed decision score function discriminates between all candidates. Hence, the proposed research provides a solid foundation for the hesitation analysis on the decision-making problem.

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Section: Introductionmentioning

confidence: 99%