An Alternative Approach to Normal Parameter Reduction Algorithm for Soft Set Theory

The soft set theory is a mathematical tool that deals with uncertainty, imprecise, and vagueness in decision systems. 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 of the decision systems. Many normal parameter reduction algorithms exist to handle parameter reduction and maintain consistency of decision choices. However, they require much time to repeatedly run the algorithm to reduce unnecessary parameters using either parameter important degree or oriented parameter sum. In this paper, we propose an alternative algorithm for parameter reduction and decision making based on soft set theory. We show that the proposed algorithm can reduce the computational complexity and run time compared with baseline algorithms. To evaluate the proposed algorithm, we perform thorough experiments on a binary-valued data set. The experimental result shows that the proposed algorithm is feasible and has relatively reduced the computational complexity and running time. In addition, the algorithm is relatively easy to understand compared with the state of the art of normal parameter reduction algorithm. The proposed algorithm is able to avoid the use of parameter important degree, decision partition, and finding the multiple of the universe within the sets.