Knowledge-Level Fusion: A Novel Information Fusion Mode From the Perspective of Granular Computing

• Published in: IEEE transactions on cybernetics Vol. 55; no. 4; pp. 1758 - 1771
• Main Authors: Zhao, Fan, Zhang, Qinghua, Yang, Ying, Yin, Longjun, Wang, Guoyin, Ding, Weiping
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2025
• Subjects: Aerospace electronics; Data mining; Data models; Decision making; Feature extraction; Granular computing; Granular computing (GrC); granulation; information fusion; knowledge-level fusion; Machine learning algorithms; Problem-solving; quotient space; Rough sets; Semantics
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2025.3538646

In recent years, with the rapid development of the Internet, multisource information fusion has become a forefront issue due to its ability to merge different information. Granular computing (GrC), as a methodology simulating human hierarchical cognition, provides a new approach for multisource information fusion. However, on one hand, the existing information fusion studies in GrC all focus on feature-level fusion and decision-level fusion based on multisource data, neglecting the basic characteristics and advantages of GrC: granulation. On the other hand, the existing methods for fusing the knowledge spaces in GrC suffer from losing the necessary information or artificially adding information. In order to address these issues, a novel information fusion mode from the perspective of GrC is proposed in this article, named knowledge-level fusion. First, by introducing a new step, that is, granulate data to construct the knowledge space, into the multisource information fusion process, the knowledge-level fusion mode is proposed. Second, the optimistic core quotient space is proposed to characterize the information consensus and information gap of multisource knowledge spaces in the static data environment. The pessimistic core quotient space is proposed to characterize the information consensus in the dynamic data environment. Related theorems are given to describe the characteristics of the core quotient spaces. Then, the knowledge-level fusion method driven jointly by the data space and the knowledge space is introduced based on the principle of extracting the core quotient space first and then allocating other objects in the candidate set. On the basis, the superiority of the proposed method over the existing methods is demonstrated through theoretical analysis. Finally, experiments on 12 UCI datasets and three UKB datasets are carried out to verify the promoting effect on classification and clustering algorithms, the effectiveness compared to feature-level and decision-level fusion modes, efficiency and statistical significance of the proposed knowledge-level fusion method and mode.