The grouping weighted averaging operator via three-way conflict analysis

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

Information Sciences Pub Date : 2025-02-19 DOI:10.1016/j.ins.2025.121990

Xiaonan Li , Rong Liang , Huangjian Yi

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引用次数: 0

Abstract

Aggregation operators play an important role in problems related to information fusion. There are various aggregation operators, and selecting appropriate ones for a specific problem remains a challenging task. For the evaluation problem in three-way conflict analysis, this paper attempts to propose a new type of aggregation operator: the grouping weighted averaging (GWA) operator. GWA operators not only consider the implicit information in data, but also do not require strong prior knowledge of data to be aggregated. First, we divide the data into groups, which correspond to coalitions in three-way conflict analysis. Second, weights of groups are generated according to their properties. Third, the final result is obtained via two aggregations: within and between groups. Besides, we also provide multiple GWA operators based on various partitions and weight allocation methods, and study their theoretical properties. Especially, as an application to conflict analysis, we propose an index of stability based on the GWA operator to compare coalition systems.

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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.