Experimental Analysis of Soft Set Based Parameter Reduction Algorithms for Decision Making
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Abstract
In the field of data mining, the parameter reduction method solves the decision making problems for the knowledge discovery process. Big data faces many problems which can be solved with the help of parameter reduction. Now a day’s reduction of data is extremely significant to make the optimal decision on the basis of some parameters. In this paper, the literature survey shows the various methods of parameter reduction which are based on the Soft Set theory. Soft set theory is based on the parameterized reduction property. This paper mainly focuses on the analysis of existing parameter based reduction methods using the soft set concept which are practically implemented with machine learning. The new soft set based approach for parameter reduction is also proposed called as ranked based parameter reduction method for the optimal selection of object to take the correct decision. For a better understanding, a comparison of various implemented algorithms is also presented.
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