Incremental information fusion in the presence of object variations for incomplete interval-valued data based on information entropy

Abstract

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.