Neighborhood entropy attribute reduction based on minimizing neighborhood mutual information

Attribute reduction is an important research content of rough set theory. Attribute reduction in hybrid information system is the main research direction at present. In the neighborhood rough set model, an improved attribute reduction algorithm based on neighborhood entropy is proposed by incorporating neighborhood mutual information entropy, because the independence between attributes is not considered. Firstly, based on the neighborhood entropy, the neighborhood mutual information entropy in hybrid information system is proposed. Then the theoretical analysis shows that the neighborhood mutual information entropy can be used to evaluate the independence of attributes. Finally, the neighborhood mutual information entropy is integrated into the traditional neighborhood entropy attribute reduction, and a neighborhood entropy attribute reduction algorithm based on minimizing neighborhood mutual information is proposed.The simulation results show that the algorithm can further improve the degree of independence of attributes in attribute reduction results, and has higher reduction performance than the related attribute reduction algorithms.