Attribute reduction based on max-decision neighborhood rough set model

The neighborhood rough set model only focuses on the consistent samples whose neighborhoods are completely contained in some decision classes, and ignores the divisibility of the boundary samples whose neighborhoods can not be contained in any decision classes. In this paper, we pay close attention to the boundary samples, and enlarge the positive region by adding the samples whose neighborhoods have maximal intersection with some decision classes. Applying the mentioned idea, we introduce a new neighborhood rough set model, named max-decision neighborhood rough set model. An attribute reduction algorithm is designed based on the model. Both theoretical analysis and experimental results show that the proposed algorithm is effective for removing most redundant attributes without loss of classification accuracy.