An object-parameter approach to predicting unknown data in incomplete fuzzy soft sets
Applied Mathematical ModellingPublished 20 September 2012
Tingquan Deng, Xiaofei Wang
Citations59
SJR quartileQ1
SJR score1.13
SNIP1.72
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TL;DR
New notions of complete distance between two objects and relative dominance degree between two parameters are introduced and an object-parameter method is proposed to predict unknown data in incomplete fuzzy soft sets.
Abstract
Incomplete data in soft sets lead to uncertainty and inaccuracy in representing and handling information. This paper introduces notions of complete distance between two objects and relative dominance degree between two parameters. Based on both the notions, an object-parameter method is proposed to predict unknown data in incomplete fuzzy soft sets. The proposal makes full use of known data, including the information from the relationship between known values of all objects on a certain parameter and the information from the relationship between known values of an object on all parameters. The effectiveness of the proposal is verified by many examples under the compared investigation of classical predicted methods.
Keywords
Computer ScienceDecision Sciences
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