OFS-Density: A novel online streaming feature selection method

Abstract Online streaming feature selection which deals with streaming features in an online manner plays a critical role in big data problems. Many approaches have been proposed to handle this problem. However, most existing methods need domain information before learning and specify some parameters in advance. In real-world applications, we cannot always require the domain information and it is a big challenge to specify uniform parameters for all different types of data sets. Motivated by this, we propose a new online streaming feature selection method based on adaptive density neighborhood relation, named OFS-Density. More specifically, with the neighborhood rough set theory, OFS-Density does not require the domain information before learning. Meanwhile, we propose a new adaptive neighborhood relation using the density information of the surrounding instances, which does not need to specify any parameters in advance. By the fuzzy equal constraint, OFS-Density can select features with a low redundancy. Finally, experimental studies on fourteen datasets show that OFS-Density is superior to traditional feature selection methods with the same numbers of features and state-of-the-art online streaming feature selection algorithms in an online manner.