Soft Set Based Parameter Value Reduction for Decision Making Application

The soft set theory is a completely new mathematical tool for modeling vagueness and uncertainty, which can be applied to decision making. However, in the process of making decision, there are some unnecessary and superfluous information which should be reduced. Normal parameter reduction is a good...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.35499-35511
Main Authors: Ma, Xiuqin, Qin, Hongwu
Format: Article
Language:English
Subjects:
Algorithms
Computation
Computational complexity
Decision making
Decision theory
Heuristic algorithms
maximal parameter value reduction
Parameter reduction
Parameter value reduction
Parameterization
Parameters
Reduction
Redundancy
Rough sets
Set theory
Soft sets
Success
Uncertainty
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Summary:The soft set theory is a completely new mathematical tool for modeling vagueness and uncertainty, which can be applied to decision making. However, in the process of making decision, there are some unnecessary and superfluous information which should be reduced. Normal parameter reduction is a good way to reduce superfluous information, which keeps the entire decision ability. However, the algorithm has a low redundant degree, which involves a great amount of computation. It is not certain that normal parameter reduction has the solution, that is, it has a low success rate of finding reduction. Parameterization value reduction is another reduction method, which improves redundant degree, amount of computation, and success rate of finding reduction. However, this method only considers the best choice, but it does not concern the suboptimal choice, the sequence of choice, and added parameter, that is, it loses some part of decision ability. In order to settle these problems, in this paper, we introduce the parameter value reduction which keeps the entire decision ability while having a very high redundant degree and success rate of finding reduction and low amount of computation. Maximal parameter value reduction is defined as the special cases of parameter value reduction and the related heuristic algorithms are presented, which reaches an extreme degree to reduce the redundant information. The comparison result among maximal parameter value reduction, parameterization value reduction, and normal parameter reduction on 30 datasets shows that the proposed algorithm outperforms parameterization value reduction and normal parameter reduction.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2905140