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A new approach for generation of generalized basic probability assignment in the evidence theory
Pattern Analysis and Applications ( IF 3.9 ) Pub Date : 2021-02-17 , DOI: 10.1007/s10044-021-00966-0
Yongchuan Tang , Dongdong Wu , Zijing Liu

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 with the characteristics of complex, unstable, uncertain, and incomplete. 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 with incomplete information. A new method is proposed to generate the generalized basic probability assignment (GBPA) based on the triangular fuzzy number model under the open-world assumption. First, the maximum, minimum, and mean values for the triangular membership function of each attribute in classification problem can be obtained to construct a triangular fuzzy number representation model. Then, by calculating the length of the intersection points between the sample and the triangular fuzzy number model, a GBPA set with an assignment for the empty set can be determined. 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 is used to verify the rationality and superiority of the proposed method.



中文翻译:

证据理论中生成广义基本概率分配的新方法

信息融合过程需要处理大量具有多源,异构,不准确,不可靠和不完整的不确定信息。在实际工程应用中,Dempster-Shafer证据理论由于其在数据融合中的有效性而被广泛用于多源信息融合。在具有复杂,不稳定,不确定和不完整特征的环境中,信息源对多源信息融合具有重要影响。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情况,并研究了具有不完整信息的基本概率分配的生成。提出了一种在开放世界假设下基于三角模糊数模型生成广义基本概率分配(GBPA)的新方法。首先,可以获取分类问题中每个属性的三角隶属函数的最大值,最小值和平均值,以构建三角模糊数表示模型。然后,通过计算样本和三角模糊数模型之间的交点的长度,可以确定带有空集的GBPA集。所提出的方法不仅可以简单灵活地用于不同的复杂环境中,而且在信息处理中的信息丢失少。最后,

更新日期:2021-02-18
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