Abstract
Interval-valued fuzzy soft set theory is a powerful tool that can provide the uncertain data processing capacity in an imprecise environment. The two existing methods for decision making based on this model were proposed. However, when there are some extreme values or outliers on the datasets based on interval-valued fuzzy soft set for making decisions, the existing methods are not reasonable and efficient, which may ignore some excellent candidates. In order to solve this problem, we give a novel approach to decision making based on interval-valued fuzzy soft set by means of the contrast table. Here, the contrast table has symmetry between the objects. Our proposed algorithm makes decisions based on the number of superior parameter values rather than score values, which is a new perspective to make decisions. The comparison results of three methods on two real-life cases show that, the proposed algorithm has superiority to the existing algorithms for the feasibility and efficiency when we face up to the extreme values of the uncertain datasets. Our proposed algorithm can also examine some extreme or unbalanced values for decision making if we regard this method as supplement of the existing algorithms.
Highlights
College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China; Citation: Qin, H.; Wang, Y.; Ma, X.; Abstract: Interval-valued fuzzy soft set theory is a powerful tool that can provide the uncertain data processing capacity in an imprecise environment
We propose a novel approach to decision making based on Interval-valued fuzzy soft set (IVFSS) by means of the contrast table, which considers the extreme values or outliers
Our proposed algorithm makes decision based on the number of superior parameter values, which is a new perspective to make decision
Summary
A Novel Approach to Decision Making BASED on Interval-Valued Fuzzy Soft Set. There exists uncertain and fuzzy data when we face up to practical and complicated problems in a lot of domains as diverse as social science, medical science, economics, engineering [1,2], and so on. The paper of [33] expressed one adjustable algorithms of decision making created on the definition of level soft sets This method transformed interval-valued data into binary data, which lost the original superiority of interval-valued description for IVFSS. When there are some extreme values or outliers on the datasets for making decisions, the methods proposed in [29,36] were not reasonable and efficient. We propose a novel approach to decision making based on IVFSS by means of the contrast table, which considers the extreme values or outliers.
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