Abstract
This paper aims to find better algorithms for solving parameter reduction problems of soft sets and gives their potential applications. Firstly, we define the matrix of dominant support parameters and use it to explain the essential reasons for the different choice values of arbitrary pair of objects. Then we propose techniques for translating the normal and pseudo parameter reduction problems of soft sets into several equivalent 0–1 linear programming models, thus reduction problems of soft sets can be solved by any computational software for integer programming. Compared with the algorithm proposed by Ma et al., experimental results show that our method for normal parameter reduction is more efficient particularly when the number of parameters is big. At last we build a software system to show the potential applications of our methods in decision support.
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