Dynamic information fusion in multi-source incomplete interval-valued information system with variation of information sources and attributes

Abstract

Interval-valued data describe the random phenomenon that abounds in the real world, a pivotal research orientation in uncertainty processing. With the rapid development of big data, we may gather information from multiple information sources. To effectively acquire knowledge from multiple information sources, information fusion is commonly used to get a unified representation. However, sometimes data gathered from multiple sources may be lost; it is meaningful and necessary to study the fusion of multi-source incomplete interval-valued data. We propose a novel information fusion method based on information entropy for multi-source incomplete interval-valued data and four incremental fusion mechanisms characterized by the change in information sources and attributes. The corresponding static and dynamic fusion algorithms are designed, and their time complexities are analyzed. Experimental results show that the proposed method outperforms the mean, max, and min fusion methods. Furthermore, the four incremental fusion mechanisms reduced the runtime compared with the static fusion mechanism.