MsIFS: Multi-Source Information Fusion Based on Information Sets

Multi-source information fusion (MSIF) is a sophisticated estimating technique that enables users to more precisely analyze 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 MSIF. In view of this, a multi-source information fusion method based on information sets (MsIFS) is proposed in this paper. First, constructing different agents according to four membership degree functions with the help of the concept of information sets. 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, a MSIF algorithm is designed by infimum-measure approach. The experimental results show the rationality and effectiveness of our proposed algorithm.