GIQ: A Generalized Intelligent Quality-Based Approach for Fusing Multisource Information

In this article, we propose a generalized intelligent quality-based approach for fusing multisource information. The goal of the proposed approach is to fuse the multicomplex-valued information while maintaining a high quality of the fused result by considering the usage of credible information sources. First, a vector representation of complex-valued distribution is defined, as well as the definitions of compatibility and conflict degrees between complex-valued distributions. Based on that, the information quality measure of complex-valued distribution is devised by leveraging the concept of Gini entropy. After that, we study some special cases of the information quality measure in maximally certain and uncertain complex-valued distributions. Additionally, a uniform fusion method for complex-valued distributions is proposed on the basis of the complex-valued information quality as an initial feasible basis of decision-making. Taking into account a credibility measure in terms of the subsets of information sources, a weighted fusion method is then presented for complex-valued distributions. Particularly, the weighted fusion method can achieve the highest quality of the fused result from the associated aggregations of information that are modeled in complex-valued distributions. Finally, some examples are illustrated to demonstrate the feasibility and effectiveness of the proposed methods.

Automated summary

This article introduces a generalized intelligent quality-based approach for fusing multisource information, with the aim of maintaining high quality fused results by considering credible information sources. The method defines vector representation and compatibility criteria for complex-valued distributions, devises an information quality measure using Gini entropy, and proposes uniform and weighted fusion methods for complex-valued distributions. These methods aim to achieve the highest quality fused results by taking into account the credibility of information sources.