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Neighborhood rough set based online streaming feature selection methods have aroused wide concern in recent years and played a vital role in processing high-dimensional data. However, most of the existing methods are directly applied to handle single-label data, or to handle multi-label data by converting multi-label data into a combination of multiple single-label datasets, which ignores that the label set of multi-label data is an integral whole. In this paper, we propose a novel online streaming feature selection for multi-label learning via the neighborhoorough set model, in which feature significance, feature redundancy, and label space integrity are taken into account, simultaneously. To be specific, we first define a new adaptive neighborhood relation to avoid the setting of neighborhood parameter and restructure the neighborhood rough set model to be suitable for processing multi-label data directly. Based …