Influence factor-based transformation method for translating mass function to probability in Dempster–Shafer evidence theory

IF 7.5 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence Pub Date : 2025-03-11 DOI:10.1016/j.engappai.2025.110385

Haocheng Shao , Lipeng Pan , Jiahui Chen , Xiaozhuan Gao , BingYi Kang

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引用次数: 0

Abstract

Dempster–Shafer evidence theory provides an effective mathematical tool to represent uncertain information by assigning information into power set. Among its associated studies, a pivotal challenge is the transformation of mass functions into probability distributions which can enhance the robustness and reliability of decision-making. In this paper, influence factor is constructed by considering the impact of transformation between multi-element propositions and single-element propositions. Then based on influence factor, the novel transformation method is proposed. In addition, some numerical examples are used to explain effectiveness of new method by analyzing the probability information capacity of different methods. Finally, this paper applies the novel method to target recognition and validates its effectiveness as well as its enhanced support for decision-making through the utilization of real-world datasets.

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来源期刊

Engineering Applications of Artificial Intelligence 工程技术-工程：电子与电气

CiteScore

9.60

自引率

10.00%

发文量

505

审稿时长

68 days

期刊介绍： Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.