Dempster-Shafer evidence Theory (DST) is a theoretical framework for uncertainty modeling and reasoning, in which modeling the Basic Belief Assignment (BBA) constitutes a crucial and challenging part. The prevailing BBA determination methods have their own pros and cons, and the joint use of them is expected to provide a better BBA. However, explicitly using several BBA determination methods and combining the BBAs through a specific fusion rule is inefficient. To address this issue, we propose a learning-based BBA modeling approach with multi-method fusion. A deep network is trained which learns the mapping from the training samples to the comprehensive BBAs obtained by jointly using the prevailing BBA modeling methods as the generalized training labels. Experimental results on remote sensing image datasets and UCI datasets demonstrate that the proposed method outperforms the individual BBA modeling methods in terms of classification performance.

In the theory of belief functions, the evidence combination is a kind of decision-level information fusion. Given two or more Basic Belief Assignments (BBAs) originated from different information sources, the combination rule is used to combine them to expect a better decision result. When only a combined BBA is given and original BBAs are discarded, if one wants to analyze the difference between the information sources, evidence de-combination is needed to determine the original BBAs. Evidence de-combination can be considered as the inverse process of the information fusion. This paper focuses on such a defusion of information in the theory of belief functions. It is an under-determined problem if only the combined BBA is available. In this paper, two optimization-based approaches are proposed to de-combine a given BBA according to the criteria of divergence maximization and information maximization, respectively. The new proposed approaches can be used for two or more information sources. Some numerical examples and an example of application are provided to illustrate and validate our approaches.