Multisource basic probability assignment fusion based on information quality. Issue 4 (3rd February 2021)

• Record Type: Journal Article
• Authors: Li, Dingbin; Deng, Yong; Cheong, Kang Hao
• Is Part Of: International journal of intelligent systems. Volume 36:Issue 4(2021)
• Page Start: 1851
• Page End: 1875
• Publication Date: 2021-02-03
• Subjects: basic probability assignment -- Dempster–Shafer theory -- gini entropy -- information quality -- source credibility -- uncertainty; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22363
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: Information quality has received extensive attention recently. Yager and Petry proposed an information quality suitable for the framework of probability theory, and proposed a method of fusing multisource information, which can improve the information quality required for decision‐making. Then, Bouhamed et al. extended information quality to the theory of possibility. However, the basic probability assignment ( BPA ) in evidence theory can deal with uncertainty more effectively. Therefore, this work provides a companion paper that makes the method applicable to evidence theory. This method uses vector notation to represent B P A . A fusion method is designed to select the best quality subset based on two factors: information quality and source credibility function, and using the score function to verify the quality of each subset. Finally, a numerical example details the eight steps of the method, and uses the Iris data set and banknote authentication data set to illustrate the application of the method in pattern recognition.