Evidence combination using OWA-based soft likelihood functions

摘要

Dempster's combination rule has been widely regarded and applied since it is an effective and rigorous method of synthesizing multisource information with its special information representation (ie, mass function or basic probability assignment). However, it has also been criticized and debated upon regarding some of its unreasonable behaviors and restrictive requirements, such as the counterintuitive results in some cases. To address these issues from different perspectives, in this study, an alternative fusion rule is developed under the framework of Dempster-Shafer evidence theory. A novel evidence combination rule called CR-SLF is proposed based on soft likelihood functions (SLF) considering the ordered weighted average aggregation operator. Some illustrative examples are shown, and the corresponding analyses demonstrate the good performance of CR-SLF to fuse multisource evidence. To extend CR-SLF further, the reliability of multisource evidence is considered from two aspects, subsequently two reliability-based combination rules are presented, including the discount-based rule and the SLF improvement-based rule. The simulation results show that the reliability-based CR-SLF has a better fusion effect than the rule without considering the reliability.