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

IF 8 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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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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Dempster-Shafer证据理论中基于影响因子的质量函数转化为概率的方法

Dempster-Shafer证据理论通过将信息分配到幂集中，为表示不确定信息提供了有效的数学工具。在其相关研究中，一个关键的挑战是将质量函数转换为概率分布，从而提高决策的鲁棒性和可靠性。本文通过考虑多元素命题与单元素命题之间转换的影响，构建了影响因子。在此基础上，提出了基于影响因子的变换方法。此外，通过分析不同方法的概率信息容量，通过数值算例说明了新方法的有效性。最后，本文将新方法应用于目标识别，并通过使用真实数据集验证了其有效性以及对决策的增强支持。

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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.