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.

中文翻译：

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基于 Dempster-Shafer 证据理论的大规模多源数据信息融合


存在许多大规模的多源数据，从遗传信息到医疗记录和军事情报。这些数据源中嵌入的固有复杂性和不确定性对信息融合过程构成了重大挑战。由于其表示数据不确定性的非凡能力，Dempster-Shafer （D-S） 证据理论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要问题：（1） 样本信息向证据的转换和基本概率分配 （BPA） 函数的构建;（2） 解决相互矛盾的证据;（3） 减轻计算中的指数爆炸。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。最初，引入了支持矩阵的概念，并将数据矩阵转换为支持矩阵，以解决与 BPA 相关的构建挑战。接下来，通过合并由平均值组成的附加数据源，引入了一种解决证据冲突的方法。此外，还提出了一种通过使用分层融合方法来缓解高计算复杂性的解决方案。最后，实验结果表明，与其他 5 种高级信息融合方法相比，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卓越的性能。