Information fusion technology plays a crucial role in integrating data from multiple sources or sensors to generate comprehensive representation, which can eliminate uncertainty in multi-source information systems (Ms-IS). Incomplete interval-valued data, a generalized form of single-valued data, is commonly encountered in real-world scenarios and effectively represents uncertain information. This paper introduces a novel information entropy specifically designed to quantify the uncertainty in incomplete interval-valued data. Based on the proposed entropy, a new unsupervised fusion approach is developed. Additionally, two dynamic update mechanisms are established to obtain fusion results efficiently when collecting new objects and removing obsolete ones. The relevant static and dynamic fusion algorithms are provided, and a detailed analysis and comparison of their time complexities are conducted. Finally, the effectiveness analysis reveals that the proposed method achieves higher average classification accuracy (5% to 8.7% improvement) compared to three common fusion methods (MAXF, MEANF, and MINF), as well as the state-of-the-art entropy-based supervised fusion method (ESF). The efficiency analysis demonstrates that the average running time of dynamic fusion algorithms is significantly lower (66.9% to 85.6% reduction) compared to the static fusion algorithm, and this difference is statistically significant.

中文翻译：

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基于信息熵的不完全区间值数据存在对象变化时的增量信息融合

信息融合技术在整合来自多个源或传感器的数据以生成综合表示方面发挥着至关重要的作用，这可以消除多源信息系统（Ms-IS）中的不确定性。不完全区间值数据是单值数据的广义形式，在现实场景中经常遇到，并且有效地表示不确定信息。本文介绍了一种新颖的信息熵，专门用于量化不完整区间值数据的不确定性。基于所提出的熵，开发了一种新的无监督融合方法。此外，还建立了两种动态更新机制，以便在收集新对象和删除过时对象时有效地获得融合结果。给出了相关的静态和动态融合算法，并对它们的时间复杂度进行了详细的分析和比较。最后，有效性分析表明，与三种常见的融合方法（MAXF、MEANF 和 MINF）相比，该方法实现了更高的平均分类精度（提高了 5% 到 8.7%），以及最先进的熵基于监督融合方法（ESF）。效率分析表明，与静态融合算法相比，动态融合算法的平均运行时间显着降低（减少66.9％至85.6％），并且这种差异具有统计显着性。