Foundational theories of hesitant fuzzy sets and hesitant fuzzy information systems and their applications for multi-strength intelligent classifiers

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences Pub Date : 2025-04-18 DOI:10.1016/j.ins.2025.122212

Shizhan Lu , Zeshui Xu , Zhu Fu , Longsheng Cheng , Tongbin Yang

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引用次数: 0

Abstract

Hesitant fuzzy sets find extensive application in specific scenarios involving uncertainty and hesitation. In the context of set theory, the concept of inclusion relationship holds significant importance as a fundamental definition. Consequently, as a type of sets, hesitant fuzzy sets necessitate a clear and explicit definition of the inclusion relationship. Based on the discrete form of hesitant fuzzy membership degrees, this study proposes multiple types of inclusion relationships for hesitant fuzzy sets. Subsequently, this paper introduces foundational propositions related to hesitant fuzzy sets, as well as propositions concerning families of hesitant fuzzy sets. Furthermore, this research presents foundational propositions regarding parameter reduction of hesitant fuzzy information systems. An example and an algorithm are provided to demonstrate the parameter reduction processes. Lastly, a multi-strength intelligent classifier is proposed for diagnosing the health states of complex systems.

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犹豫模糊集和犹豫模糊信息系统的基本理论及其在多强度智能分类器中的应用

犹豫模糊集在涉及不确定性和犹豫的特定场景中有着广泛的应用。在集合论的背景下，包含关系的概念作为一个基本定义具有重要意义。因此，犹豫模糊集作为集合的一类，需要明确定义包含关系。基于犹豫模糊隶属度的离散形式，提出了犹豫模糊集的多种包含关系。随后，介绍了与犹豫模糊集相关的基本命题，以及关于犹豫模糊集族的命题。在此基础上，提出了犹豫模糊信息系统参数约简的基本命题。给出了一个示例和算法来演示参数约简过程。最后，提出了一种用于复杂系统健康状态诊断的多强度智能分类器。

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来源期刊

Information Sciences 工程技术-计算机：信息系统

CiteScore

14.00

自引率

17.30%

发文量

1322

审稿时长

10.4 months

期刊介绍： Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.