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A modified soft‐likelihood function based on POWA operator
Author(s) -
Mi Xiangjun,
Tian Ye,
Kang Bingyi
Publication year - 2020
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.22228
Subject(s) - operator (biology) , function (biology) , probabilistic logic , mathematics , computer science , likelihood function , fuse (electrical) , relation (database) , field (mathematics) , perspective (graphical) , information fusion , artificial intelligence , mathematical optimization , algorithm , data mining , estimation theory , biochemistry , chemistry , repressor , evolutionary biology , biology , pure mathematics , transcription factor , electrical engineering , gene , engineering
Information fusion is an important research direction. In this field, there are plenty of ways to combine evidence. Initially, Yager proposed a soft‐likelihood function based on the ordered weighted average (OWA) operator to effectively fuse compatible probabilistic evidence. Recently, Song et al proposed a new soft‐likelihood function based on the power ordered weighted average (POWA) operator. However, through analysis, we find Song et al's method has the following two shortcomings: (a) The weight of POWA cannot comprehensively reflect the relation between probability and OWA operator. (b) The soft‐likelihood function does not reflect the preferences of decision makers. To overcome the above problem, we propose a modified soft‐likelihood function. The effectiveness of the proposed method is demonstrated from the perspective of theoretical analysis and numerical examples.