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A Review on Soft Set-Based Parameter Reduction and Decision Making
- Source :
- IEEE Access, Vol 5, Pp 4671-4689 (2017)
- Publication Year :
- 2017
- Publisher :
- IEEE, 2017.
-
Abstract
- Many real world decision making problems often involve uncertainty data, which mainly originating from incomplete data and imprecise decision. The soft set theory as a mathematical tool that deals with uncertainty, imprecise, and vagueness is often employed in solving decision making problem. It has been widely used to identify irrelevant parameters and make reduction set of parameters for decision making in order to bring out the optimal choices. In this paper, we present a review on different parameter reduction and decision making techniques for soft set and hybrid soft sets under unpleasant set of hypothesis environment as well as performance analysis of the their derived algorithms. The review has summarized this paper in those areas of research, pointed out the limitations of previous works and areas that require further research works. Researchers can use our review to quickly identify areas that received diminutive or no attention from researchers so as to propose novel methods and applications.
- Subjects :
- 0209 industrial biotechnology
Weighted sum model
General Computer Science
review
02 engineering and technology
Machine learning
computer.software_genre
decision making
020901 industrial engineering & automation
Business decision mapping
0202 electrical engineering, electronic engineering, information engineering
Influence diagram
soft set
General Materials Science
hybrid soft sets
Mathematics
Decision engineering
business.industry
Dominance-based rough set approach
General Engineering
Parameter reduction
020201 artificial intelligence & image processing
Artificial intelligence
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
computer
lcsh:TK1-9971
Soft set
Decision analysis
Optimal decision
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 5
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....155d3f4513f51951777b263e0661046e