Incremental attribute reduction is a data mining method for dynamic environment. The incremental attribute reduction algorithm already proposed is only applicable to symbolic information systems. However

there are few related studies on mixed information systems

which promotes the construction of the related incremental attribute reduction algorithm under the mixed information system. The discernibility degree is an important method used for designing attribute reduction. In this paper

the traditional discernibility degree is generalized under the mixed information system

and the concept of neighborhood discernibility degree is presented. Then

the incremental learning of neighborhood discernibility degree is studied respectively when objects increase or objects decrease under the mixed information system. Finally

according to this incremental learning

the corresponding incremental attribute reduction algorithms are proposed

respectively. The related experimental results on the UCI data set show that the proposed incremental attribute reduction can update the reduction results more quickly than the non incremental attribute reduction.