PSO-NRS: an online group feature selection algorithm based on PSO multi-objective optimization

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.