Evidence representation of uncertain information on a frame of discernment with semantic association

Belief functions as a powerful model to represent and deal with uncertain information are widely used in information fusion. However, semantic association within a frame of discernment is not well defined in traditional framework of belief function theory. To solve the problem, in this work models and methods for evidence representation of uncertain information on a frame of discernment with semantic association are studied. These contributions are made in the study. At first, a formal definition for the concept of semantic association is proposed via an axiomatic manner. Second, new evidence representation models including belief, plausibility and commonality measures, are designed with the consideration of semantic association. Third, a novel evidence discounting operation, called associative discounting, is proposed to amend original evidence in terms of more refined meta-knowledge regarding the reliability of information sources. The effectiveness of proposed models and methods is verified through practical applications on UCI (University of California, Irvine) data sets for the combination of multiple classifiers. This work, on the one hand, has successfully imported the concept of semantic association into the framework of belief function theory; on the other hand, it provides a new scheme to correct original information in a more refined manner.