GEJS: A Generalized Evidential Divergence Measure for Multisource Information Fusion

Abstract

Multisource information fusion (MSIF) technologies play an important role in various fields and practical applications. As a useful methodology to represent and handle uncertain information, the Dempster–Shafer evidence theory has been broadly used in many fields of MSIF. In evidence theory, however, Dempster’s combination rule (DCR) may result in counterintuitive results when fusing highly conflicting evidence. To address this issue, in this article, a novel MSIF method based on a newly defined generalized evidential divergence measure among multiple sources of evidence is proposed for decision making. Specifically, we first design a new generalized evidential Jensen–Shannon (GEJS) divergence to measure the conflict and discrepancy among multiple sources of evidence. The proposed GEJS divergence has three main characteristics, which are beneficial for evidential divergence measurement: 1) it measures the divergence among multiple sources of evidence, not just two, and thus provides a more generalized framework; 2) it considers different weights of multiple sources of evidence to measure divergence among them, which is desirable for application requirements; and 3) it considers the cardinality of propositions of multiple sources of evidence to measure divergence by considering features of evidential subsets. Given these advantages of GEJS, an appropriate weight for each source of evidence can be obtained. Next, according to the corresponding weights, we amend the bodies of evidence to generate a weighted average evidence. DCR is then used to fuse the weighted average evidence, and the final result is used to support decision making. Finally, we present a case study of fault diagnosis and a sensitivity analysis to demonstrate that the proposed MSIF is effective and robust for addressing conflicting situations compared to related works. Additionally, an application of classification validates the practicability of the proposed MSIF with a good decision level.