View in EDS

Construction of cosine-based intuitionistic fuzzy similarity measures: Applications in decision making and attribute reduction.

Saved in:
Bibliographic Details
Title: Construction of cosine-based intuitionistic fuzzy similarity measures: Applications in decision making and attribute reduction.
Authors: Khan, Muhammad Jabir1 (AUTHOR) jabirkhan@ntu.edu.cn, Akram, Muhammad2 (AUTHOR) m.akram@pucit.edu.pk, Alreshidi, Nasser Aedh3 (AUTHOR) nasser.alreshidi@nbu.edu.sa, Ding, Weiping1,4 (AUTHOR) ding.wp@ntu.edu.cn
Source: Expert Systems with Applications. May2026, Vol. 311, pN.PAG-N.PAG. 1p.
Subjects: Decision making, Feature selection, Fuzzy sets, Renewable energy sources, TOPSIS method, Entropy (Information theory), Rank correlation (Statistics)
Abstract: This paper presents a unified framework for constructing cosine-based similarity measures for intuitionistic fuzzy sets and demonstrates their effectiveness in decision-making and attribute reduction. A generator-based approach is introduced to derive a broad family of similarity measures from existing ones, complemented by alternative constructions using dissimilarity measures and functions. A general criterion for transforming similarity measures into entropy measures is established, with counterexamples provided to delineate its limitations. To validate the proposed methods, the similarity measures are integrated into the TOPSIS framework with entropy-based weighting and demonstrated through a renewable energy project selection example. In addition, an attribute reduction algorithm, enhanced through TOPSIS and Spearman's correlation, is developed to improve computational efficiency and decision quality. A systematic framework for comparing similarity measures across multiple evaluation metrics, demonstrated on a specific case study, is also proposed. Numerical experiments confirm the robustness and practical value of the approach. [ABSTRACT FROM AUTHOR]
Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:This paper presents a unified framework for constructing cosine-based similarity measures for intuitionistic fuzzy sets and demonstrates their effectiveness in decision-making and attribute reduction. A generator-based approach is introduced to derive a broad family of similarity measures from existing ones, complemented by alternative constructions using dissimilarity measures and functions. A general criterion for transforming similarity measures into entropy measures is established, with counterexamples provided to delineate its limitations. To validate the proposed methods, the similarity measures are integrated into the TOPSIS framework with entropy-based weighting and demonstrated through a renewable energy project selection example. In addition, an attribute reduction algorithm, enhanced through TOPSIS and Spearman's correlation, is developed to improve computational efficiency and decision quality. A systematic framework for comparing similarity measures across multiple evaluation metrics, demonstrated on a specific case study, is also proposed. Numerical experiments confirm the robustness and practical value of the approach. [ABSTRACT FROM AUTHOR]
ISSN:09574174
DOI:10.1016/j.eswa.2026.131324