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Online streaming feature selection plays an important role in dealing with multi-dimensional data problems. Many online streaming feature selection algorithms have been combined with evolutionary algorithms (EA) and play an important role, however, most of them use single-objective optimization which has some limitations. Meanwhile, they ignore the interaction between features. The combination of features with each other may generates higher relevance. Therefore, this paper proposes a new online group feature selection algorithm PSO-NRS by fusing particle swarm optimization (PSO) algorithm and neighborhood rough set theory (NRS). PSO-NRS is able to select the set of features that are highly correlated with labels by combining features randomly. Using NRS for online feature selection does not require any domain knowledge, which makes PSO-NRS generalize better and can handle different types of data. PSO-NRS applies two layers of filtering for online feature selection. In the first filtering layer, two objective functions are designed and multi-objective optimization by particle swarm is used to select the set of features with the highest relevance. In the second filtering layer, a search strategy is defined using a rough set-based evaluation method to complete the final feature selection. The interactions between features are considered and redundant features are removed during the two filtering layers. Finally, PSO-NRS is experimented on 14 different types of datasets and compared with six state-of-the-art online feature selection algorithms to strongly validate the effectiveness and generalization of this algorithm. [ABSTRACT FROM AUTHOR]

Multi-label feature selection has been essential in many big data applications and plays a significant role in processing high-dimensional data. However, the existing online stream feature selection methods ignore the existence of missing labels. Inspired by the neighborhood rough set that does not require prior knowledge of the feature space, we propose a novel online multi-label stream feature selection algorithm called OFS-Mean. We define a neighborhood relationship that can automatically select an appropriate number of neighbors. Without any prior space and parameters, the algorithm's performance of the algorithm is improved by real-time online prediction of missing labels based on the similarity between the instance and its neighbors. The proposed OFS-Mean divides the feature selection process into two stages: online feature importance evaluation and online redundancy update to screen important features. With the support of neighborhood rough set, the proposed OFS-Mean can adapt to various types of datasets, improving the algorithm generalization ability. In the experiment, the similarity test is used to verify the prediction results; the comparison with the traditional semi-supervised feature selection method under the condition of selecting the same number of features has achieved ideal results. [ABSTRACT FROM AUTHOR]