4.7 Article

An extension of rule base evidential reasoning in the interval-valued intuitionistic fuzzy setting applied to the type 2 diabetes diagnostic

期刊

EXPERT SYSTEMS WITH APPLICATIONS
卷 201, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2022.117100

关键词

Interval-valued intuitionistic fuzzy extension; Dempster-Shafer theory of evidence; Rule base evidential reasoning

资金

  1. Ministry of Science and Higher Education, Poland through the Regional Initiative of Excellence Program [020/RID/2018/19]

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The paper introduces a new method of rule base evidential reasoning based on interval-valued intuitionistic fuzzy values, focusing on competing fuzzy categories and proposing a new mathematical object called belief interval bounded belief interval (BIBBI) for a more reliable representation of interval-valued intuitionistic fuzzy values.
This paper presents an interval-valued intuitionistic fuzzy extension of rule base evidential reasoning which generally is a synthesis of fuzzy logic, the Dempster-Shafer theory of evidence (DST) and Atanassov's intuitionistic fuzzy sets (A-IFS) theory redefined in the framework of DST. A lot of attention is paid to situations when in the solution of decision making problem the competing fuzzy classes with intersecting membership functions in antecedent parts of fuzzy rules, e.g., such as Low and Moderate play an important role in the problem formulation. As a result, a new mathematical object belief interval bounded belief interval'' (BIBBI) is obtained. The properties of BIBBI make it possible to use it as a more reliable representation of interval-valued intuitionistic fuzzy value than that formulated in terms of A-IFS. Using the introduced DST based definition of interval-valued A-IFS, the corresponding operations with BIBBIs and the DST representation of rule base evidential reasoning in the intuitionistic fuzzy setting, a new approach to the interval-valued intuitionistic fuzzy extension of rule base evidential reasoning is developed. To prove the correctness and applicability of this approach to the solution of decision making problems, a case study of its use for the type 2 diabetes diagnostics is analyzed.

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