Multi-Label Attribute Reduction Based on Variable Precision Fuzzy Neighborhood Rough Set

Abstract

Multi-label attribute reduction as a common dimensionality reduction technique has obtained widely research in recent years. Most existing multi-label attribute reduction methods adopt discretization to deal with mixed data and have strict requirements on the condition of sample classification. However, the process of discretization may lead to information loss, moreover, strict conditions will increase the possibility of a sample classified into a wrong class. Based on this, we construct a multi-label attribute reduction method based on variable precision fuzzy neighborhood rough set. The main motivation is that the variable precision fuzzy neighborhood rough set can process multiple types of data without discretization and tolerate noisy data. Specifically, we first use the parameterized fuzzy neighborhood granule to define the fuzzy decision and decision class of each sample under different labels. Then, the fuzzy decision and decision classes under different labels are fused into the entire multi-label learning space. Finally, a multi-label attribute reduction algorithm is designed according to the defined maximum attribute significance criterion. Our experiments are conducted on a series of multi-label datasets, and the experimental results verify that the proposed algorithm achieves better classification performance than other state-of-the-art comparison algorithms.