Learning-based BBA modeling approach with multi-method fusion

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.