Attribute Reduction of Variable Precision Fuzzy Rough Set Based on Misclassification Cost

Abstract

Attribute reduction is a hot research issue in rough set.In this paper, how to reduce redundant attributes without increasing the misclassification cost is studied.Firstly, the minimum misclassification degree of variable precision fuzzy rough sets is defined.Then, by introducing the decision process, the variable precision fuzzy rough set model is proposed based on the minimum misclassification degree.Then, a heuristic attribute reduction algorithm is proposed by taking the misclassification cost as an invariant.We compare this algorithm with other algorithms through experiments.The results show that the attribute reduction results obtained by the proposed algorithm have the advantages of less reserved attributes and lower misclassification cost.

Keywords

• rough set|variable precision fuzzy rough set|misclassification cost|attribute reduction