Affiliations: [a] School of Scientific research office, Guangxi University of Finance and Economics, Nanning, China
| [b] School of Computing and Artificial Intelligence, Southwest JiaoTong University, Chengdu, Sichuan, China
Correspondence:
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Corresponding author. Pengfei Zhang, School of Computing and Artificial Intelligence, Southwest JiaoTong University, Chengdu, Sichuan 611756, China. E-mail: feifeihappy55@163.com.
Abstract: Multi-source information fusion is a sophisticated estimating technique that enables users to analyze more precisely complex situations by successfully merging key evidence in the vast, varied, and occasionally contradictory data obtained from various sources. Restricted by the data collection technology and incomplete data of information sources, it may lead to large uncertainty in the fusion process and affect the quality of fusion. Reducing uncertainty in the fusion process is one of the most important challenges for information fusion. In view of this, a multi-source information fusion method based on information sets (MSIF) is proposed in this paper. The information set is a new method for the representation of granularized information source values using the entropy framework in the possibilistic domain. First, four types of common membership functions are used to construct the possibilistic domain as the information gain function (or agent). Then, Shannon agent entropy and Shannon inverse agent entropy are defined, and their summation is used to evaluate the total uncertainty of the attribute values and agents. Finally, an MSIF algorithm is designed by infimum-measure approach. The experimental results show that the performance of Gaussian kernel function is good, which provides an effective method for fusing multi-source numerical data.
Keywords: Multi-source information fusion, information sets, Shannon entropy, uncertainty, fuzzy membership degree
