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TDBF: Two‐dimensional belief function
Author(s) -
Li Yangxue,
Deng Yong
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.22135
Subject(s) - discounting , computer science , measure (data warehouse) , function (biology) , artificial intelligence , mathematical optimization , algorithm , mathematics , data mining , evolutionary biology , biology , finance , economics
How to efficiently handle uncertain information is still an open issue. In this paper, a new method to deal with uncertain information, named as two‐dimensional belief function (TDBF), is presented. A TDBF has two components, T = ( m A , m B ), both m A and m B are classical belief functions, while m B is a measure of reliable of m A . The definition of TDBF and the discounting algorithm are proposed. Compared with the classical discounting model, the proposed TDBF is more flexible and reasonable. Numerical examples are used to show the efficiency and application of the proposed method.