Dynamic Fusion of Multisource Interval-Valued Data by Fuzzy Granulation

• Published in: IEEE transactions on fuzzy systems Vol. 26; no. 6; pp. 3403 - 3417
• Main Authors: Huang, Yanyong, Li, Tianrui, Luo, Chuan, Fujita, Hamido, Horng, Shi-Jinn
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-12-2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Data integration; Data sources; Decision making; Deletion; Fuses; Fuzzy granulation; Fuzzy set theory; Fuzzy sets; Granulation; Heuristic algorithms; incremental fusion; information fusion; Knowledge representation; Meteorology; Multisensor fusion; multisource interval-valued data; Rough sets; Uncertainty
• ISSN: 1063-6706; 1941-0034
• DOI: 10.1109/TFUZZ.2018.2832608

Information fusion is capable of fusing and transforming multiple data derived from different sources to provide a unified representation for centralized knowledge mining that facilitates effective decision-making, classification and prediction, etc. Multisource interval-valued data, characterizing the uncertainty phenomena in the data in the form of intervals in different sources, are the most common symbolic data which widely exist in many real-world applications. This paper concentrates on efficient fusing of multisource interval-valued data with the dynamic updating of data sources, involving the addition of new sources and deletion of obsolete sources. We propose a novel data fusion method based on fuzzy information granulation, which translates multisource interval-valued data into trapezoidal fuzzy granules. Given this effectively fusing capability, we develop incremental mechanisms and algorithms for fusing multisource interval-valued data with a dynamic variation of data sources. Finally, extensive experiments are carried out to verify the effectiveness of the proposed algorithms when comparing to six different fusion algorithms. Experimental results show that the proposed fusion method outperforms other related approaches. Furthermore, the proposed incremental fusion algorithms can reduce the computing overhead in comparison with the static fusion algorithm when adding and deleting multiple data sources.