A Basic Probability Assignment Generation Method Based on Normal Cloud Similarity and Its Application in Evidence Combination

IF 3.7 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Intelligent Systems Pub Date : 2025-07-23 DOI:10.1155/int/8839165

Nuo Cheng, Xin Wang

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Abstract

The effective utilization of Dempster–Shafer (D-S) evidence theory depends on the accurate establishment of the basic probability assignment (BPA). How to generate more effective BPA for different situations is always an open and hot topic. In this study, we present an approach for obtaining BPA based on the normal cloud model called combined fuzzy similarity measure (CFSM). The method first constructs the normal cloud model of each class of sample in each attribute by an interval number and uses the mean standard deviation to obtain the interval number for the test sample, thereby obtaining the normal cloud model. Then, the similarity between the test samples and the training samples is quantified based on the area relationship, thereby obtaining the BPA of the test samples. Finally, the evidence combination method based on the intuitionistic fuzzy earth mover’s distance (IFEMD) is used for experimental analysis. The experimental results verify the effectiveness of the method and its applicability in the case of small sample data.

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基于正态云相似度的基本概率赋值生成方法及其在证据组合中的应用

邓普斯特-谢弗证据理论的有效运用取决于基本概率赋值的准确建立。如何在不同情况下产生更有效的双酚a一直是一个开放和热门的话题。在本研究中，我们提出了一种基于常规云模型的双酚a提取方法，称为组合模糊相似测度（CFSM）。该方法首先通过区间数构建每个属性中每一类样本的正态云模型，并利用平均标准差得到测试样本的区间数，从而得到正态云模型。然后，根据面积关系对测试样本与训练样本的相似度进行量化，从而得到测试样本的BPA。最后，采用基于直觉模糊推土机距离（IFEMD）的证据组合方法进行实验分析。实验结果验证了该方法的有效性及其在小样本数据情况下的适用性。

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来源期刊

International Journal of Intelligent Systems 工程技术-计算机：人工智能

CiteScore

11.30

自引率

14.30%

发文量

304

审稿时长

9 months

期刊介绍： The International Journal of Intelligent Systems serves as a forum for individuals interested in tapping into the vast theories based on intelligent systems construction. With its peer-reviewed format, the journal explores several fascinating editorials written by today''s experts in the field. Because new developments are being introduced each day, there''s much to be learned — examination, analysis creation, information retrieval, man–computer interactions, and more. The International Journal of Intelligent Systems uses charts and illustrations to demonstrate these ground-breaking issues, and encourages readers to share their thoughts and experiences.