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A new probability transformation method based on a correlation coefficient of belief functions
Author(s) -
Jiang Wen,
Huang Chan,
Deng Xinyang
Publication year - 2019
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22098
Subject(s) - transformation (genetics) , probability distribution , mathematics , correlation coefficient , basis (linear algebra) , measure (data warehouse) , joint probability distribution , probability mass function , distribution (mathematics) , dempster–shafer theory , correlation , computer science , mathematical optimization , algorithm , statistics , data mining , mathematical analysis , biochemistry , chemistry , geometry , gene
Abstract The Dempster‐Shafer evidence theory is widely used in many fields of information fusion because of its advantage in handling uncertain information. One of the key issues in this theory is how to make decision based on a basic probability assignment (BPA). Currently, a feasible scheme is transforming a BPA to a distribution of probabilities. However, little attention was paid to the correlation between BPA and probability distribution. In this paper, a novel method about the probability transformation based on a correlation coefficient of belief functions is proposed. The correlation coefficient is a new measurement, which can effectively measure the correlation between BPAs. The proposed method aims at maximizing the correlation coefficient between the given BPA and the transformed probability distribution. On the basis of this idea, the corresponding probability distribution can be obtained and could reflect the original information of the given BPA to the maximum extent. It is valid to consider that the proposed probability transformation method is reasonable and effective. Numerical examples are given to show the effectiveness of the proposed method.