Abstract: Aiming at the problem that fuzzy neighborhood rough set is sensitive to data distribution and cannot effectively evaluate the classification uncertainty of datasets with large density differences,this paper proposes an attribute reduction algorithm based on fuzzy neighborhood relative decision entropy.Firstly,the classification uncertainty of the sample is defined by using the relative distance,thus remodeling the fuzzy neighborhood similarity relationship.Combined with the variable precision fuzzy neighborhood rough approximation,the possibility of the sample being classified into the wrong category is reduced.Secondly,the information entropy is augmented with the fuzzy neighborhood credibility and coverage under the information view,and this is integrated with the fuzzy neighborhood relative dependence constructed based on the algebraic view to introduce the fuzzy neighborhood relative decision entropy.Finally,an attribute reduction algorithm based on the fuzzy neighborhood relative decision entropy is designed to evaluate the importance of attributes from both the information and algebraic viewpoints.Comparative experiments with six existing attribute reduction algorithms on eight public datasets show that the proposed algorithm can effectively measure the uncertainty of samples under different data distributions and improve the classification performance of data.

Key words: Attribute reduction, Fuzzy neighborhood rough set, Classification uncertainty, Information entropy