关键词: 基本信度分配, 证据理论, 深度学习, 模式分类, 数据驱动

Abstract: Dempster-Shafer evidence theory (DST) is a theoretical framework for uncertainty modeling and reasoning, with modeling the basic belief assignment (BBA) as one of its most crucial and challenging steps. The prevailing BBA determination methods have their own pros and cons, and the joint use of them is expected to provide a better BBA. Explicitly using several BBA determination methods and combining the BBAs through a specific fusion rule is inefficient. Therefore, a learning-based BBA modeling approach with multi-method fusion is proposed in this paper. 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.

Key words: Basic Belief Assignment (BBA), Evidence Theory, Deep Learning, Pattern Classification, data-driven