Multi-Label Attribute Reduction Based on Neighborhood Multi-Target Rough Sets

The rough set model has two symmetry approximations called upper approximation and lower approximation, which correspond to a concept's intension and extension, respectively. Multi-label learning enforces the rough set model, which wants to be applied considering the correlations among labels, while the target concept should not be limited to only one. This paper proposes a multi-target model considering label correlation (Neighborhood Multi-Target Rough Sets, NMTRS) and proposes an attribute reduction approach based on NMTRS. First, some definitions of NMTRS are introduced. Second, some properties of NMTRS are discussed. Third, some discussion about the attribute significance measure is given. Fourth, the attribute reduction approaches based on NMTRS are proposed. Finally, the efficiency and validity of the designed algorithms are verified by experiments. The experiments show that our algorithm shows considerable performance when compared to state-of-the-art approaches.