Abstract
As we move into the information age, the amount of data in various fields has increased dramatically, and data sources have become increasingly widely distributed. The corresponding phenomenon of missing data is increasingly common, and it leads to the generation of incomplete multi-source information systems. In this context, this paper’s proposal aims to address the limitations of rough set theory. We study the method of multi-source fusion in incomplete multi-source systems. This paper presents a method for fusing incomplete multi-source systems based on information entropy; in particular, by comparison with another method, our fusion method is validated. Furthermore, extensive experiments are conducted on six UCI data sets to verify the performance of the proposed method. Additionally, the experimental results indicate that multi-source information fusion approaches significantly outperform other approaches to fusion.
Highlights
Information fusion is used to obtain more accurate and definite inferences from the data provided by any single information source by integrating multiple information sources; several definitions have been proposed in the literature [1,2,3,4,5,6,7,8,9]
In order to reduce the amount of information loss in the process of information system fusion, we proposed the method which used information entropy to fuse incomplete information systems
We review some basic concepts relating to rough set theory, incomplete information systems, incomplete decision systems, and conditional entropy (CE) in incomplete decision systems
Summary
Information fusion is used to obtain more accurate and definite inferences from the data provided by any single information source by integrating multiple information sources; several definitions have been proposed in the literature [1,2,3,4,5,6,7,8,9]. Rough set theory—which was introduced by Pawlak [16,17,18,19,20]—is an extension of classical set theory In data analysis, it can be considered a mathematical and soft computational tool to handle imprecision, vagueness, and uncertainty. Dong et al [24] researched the processing of information fusion based on rough set theory. Yuan et al [28] considered multi-sensor information fusion based on rough set theory. Lin et al studied an information fusion approach based on combining multi-granulation rough sets with evidence theory [32]. Jin et al [34] studied feature selection in incomplete multi-sensor information systems based on positive approximation in rough set theory.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
