论文标题
在证据理论中生成广义基本概率分配的新方法
A new approach for generation of generalized basic probability assignment in the evidence theory
论文作者
论文摘要
信息融合的过程需要处理大量的不确定信息，并具有多源，异质性，不准确性，不可靠性和不完整性。在实际的工程应用中，Dempster-Shafer证据理论由于其在数据融合中的有效性而广泛用于多源信息融合中。在复杂，不稳定，不确定和不完整的环境中，信息源对多源信息融合有重要影响。为了解决多源信息融合问题，本文考虑了从封闭世界到开放世界假设的不确定信息建模的情况，并研究了基本概率分配（BPA）的生成，但信息不完整。在本文中，提出了一种新方法来基于开放世界假设下的三角模糊数模型生成广义基本概率分配（GBPA）。所提出的方法不仅可以简单且灵活地在不同的复杂环境中使用，而且信息处理中的信息损失较少。最后，基于UCI数据集的一系列全面实验用于验证所提出方法的合理性和优越性。
The process of information fusion needs to deal with a large number of uncertain information with multi-source, heterogeneity, inaccuracy, unreliability, and incompleteness. In practical engineering applications, Dempster-Shafer evidence theory is widely used in multi-source information fusion owing to its effectiveness in data fusion. Information sources have an important impact on multi-source information fusion in an environment of complex, unstable, uncertain, and incomplete characteristics. To address multi-source information fusion problem, this paper considers the situation of uncertain information modeling from the closed world to the open world assumption and studies the generation of basic probability assignment (BPA) with incomplete information. In this paper, a new method is proposed to generate generalized basic probability assignment (GBPA) based on the triangular fuzzy number model under the open world assumption. The proposed method can not only be used in different complex environments simply and flexibly, but also have less information loss in information processing. Finally, a series of comprehensive experiments basing on the UCI data sets are used to verify the rationality and superiority of the proposed method.
