Neighborhood Discernibility Degree Incremental Attribute Reduction Algorithm for Mixed Data

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. © 2020, Chinese Institute of Electronics. All right reserved.