Attribute reduction based on k-nearest neighborhood rough sets

Abstract

Neighborhood rough sets are widely used as an effective tool to deal with numerical data. However, most of the existing neighborhood granulation models cannot well describe the neighborhoods of category-mixed samples when they are used to characterize the classification ability of a subset of attributes. In this paper, we propose a new neighborhood rough set model called k-nearest neighborhood rough sets. This model combines the advantages of both δ-neighborhood and k-nearest neighbor, and has a better ability to deal with this type of heterogeneous data than the existing models. We employ an iterative strategy to define rough approximations of a decision and discuss their monotonicity. Furthermore, an attribute reduction algorithm based on this model is designed. Experimental analysis shows that the proposed algorithm has better performance than some existing algorithms, especially the δ-neighborhood rough set model and k-nearest neighbor rough set model.