Information fusion for large-scale multi-source data based on the Dempster-Shafer evidence theory

IF 14.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Qinli Zhang , Pengfei Zhang , Tianrui Li
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引用次数: 0

Abstract

There exists many large-scale multi-source data, ranging from genetic information to medical records, and military intelligence. The inherent intricacies and uncertainties embedded within these data sources pose significant challenges to the process of information fusion. Owing to its exceptional capacity to represent data uncertainty, Dempster-Shafer (D-S) evidence theory has emerged as a widely utilized approach in information fusion. However, the evidence theory encounters three significant issues when applied to multi-source data information fusion: (1) the conversion of sample information into evidence and the construction of the basic probability assignment (BPA) function; (2) the resolution of conflicting evidence; and (3) the mitigation of exponential explosion in computation. Addressing the aforementioned challenges, this paper delves into the information fusion strategies for large-scale multi-source data based on Dempster-Shafer evidence theory. Initially, the concept of support matrix is introduced and the data matrix is transformed into a support matrix to address the construction challenges associated with BPA. Next, a method for addressing evidence conflicts is introduced by incorporating an additional data source composed of average values. Furthermore, a solution for mitigating high computational complexity is presented through the utilization of a hierarchical fusion approach. Finally, experimental results show that compared with other five advanced information fusion methods, our information method has improved the classification accuracy by 4.66% on average and reduced the time by 66.35% on average. Hence, our method is both efficient and effective, demonstrating exceptional performance in information fusion.
基于 Dempster-Shafer 证据理论的大规模多源数据信息融合
目前存在许多大规模多源数据,从遗传信息到医疗记录和军事情报,不一而足。这些数据源固有的复杂性和不确定性给信息融合过程带来了巨大挑战。由于 Dempster-Shafer(D-S)证据理论在表示数据不确定性方面的卓越能力,它已成为信息融合中广泛使用的方法。然而,证据理论在应用于多源数据信息融合时遇到了三个重大问题:(1) 将样本信息转换为证据并构建基本概率赋值(BPA)函数;(2) 解决证据冲突;(3) 缓解计算中的指数爆炸。针对上述挑战,本文基于 Dempster-Shafer 证据理论,深入探讨了大规模多源数据的信息融合策略。首先,本文引入了支持矩阵的概念,并将数据矩阵转化为支持矩阵,以解决与 BPA 相关的构建难题。接着,介绍了一种解决证据冲突的方法,即加入一个由平均值组成的额外数据源。此外,还介绍了一种通过使用分层融合方法来降低高计算复杂性的解决方案。最后,实验结果表明,与其他五种先进的信息融合方法相比,我们的信息方法平均提高了 4.66% 的分类准确率,平均缩短了 66.35% 的时间。因此,我们的方法既高效又有效,在信息融合方面表现出了卓越的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Information Fusion
Information Fusion 工程技术-计算机:理论方法
CiteScore
33.20
自引率
4.30%
发文量
161
审稿时长
7.9 months
期刊介绍: Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.
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