证据理论中广义基本概率赋值的一种新方法
A new approach for generation of generalized basic probability assignment in the evidence theory
摘要：
信息融合过程需要处理大量的多源、异构、不精确、不可靠、不完全的不确定信息。在实际工程应用中，证据理论由于其在数据融合中的有效性而被广泛应用于多源信息融合中。在复杂、不稳定、不确定、不完备的环境中，信息源对多源信息融合有着重要的影响。针对多源信息融合问题，考虑不确定信息建模从封闭世界到开放世界的假设，研究了不完全信息下的基本概率分配（BPA）生成问题。在开放世界假设下，提出了一种基于三角模糊数模型的广义基本概率分配（GBPA）生成方法。该方法不仅简单、灵活，而且在信息处理中信息损失小。最后，基于UCI数据集进行了一系列综合实验，验证了该方法的合理性和优越性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
