Multi-label feature selection based on label distribution and neighborhood rough set

Abstract

Multi-label feature selection is an indispensable technology in multi-semantic high-dimensional data preprocessing, which has been brought into focus in recent years. However, most existing methods explicitly assume that the significance of all relevant labels is the same for every instance, while ignoring the real scenarios that the significance of available labels to each instance is usually different. In this paper, we propose a novel multi-label feature selection based on label distribution and neighborhood rough set, known as LDRS. To be specific, we first construct a label enhancement method based on instance information distribution to convert the logical labels of multi-label data into label distribution, thereby capturing label significance to provide additional information for learning tasks. Then, we extend the neighborhood rough set model for label distribution learning, and discuss the related properties in detail. This extended model can effectively avoid the selection of neighborhood granularity and seamlessly apply to handle label distribution data. After that, two feature significance measures are established to realize the quality evaluation of features and the fusion of label-specific features. Finally, a novel feature selection framework is designed, which takes into account feature significance, label significance, and label-specific features, simultaneously. Experiments on both public and real-world datasets exhibit the advantages of the proposed method.