Online streaming feature selection using adapted Neighborhood Rough Set

Abstract

Online streaming feature selection, as a new approach which deals with feature streams in an online manner, has attracted much attention in recent years and played a critical role in dealing with high-dimensional problems. However, most of the existing online streaming feature selection methods need the domain information before learning and specifying the parameters in advance. It is hence a challenge to select unified and optimal parameters before learning for all different types of data sets. In this paper, we define a new Neighborhood Rough Set relation with adapted neighbors named the Gap relation and propose a new online streaming feature selection method based on this relation, named OFS-A3M. OFS-A3M does not require any domain knowledge and does not need to specify any parameters in advance. With the “maximal-dependency, maximal-relevance and maximal-significance” evaluation criteria, OFS-A3M can select features with high correlation, high dependency and low redundancy. Experimental studies on fifteen different types of data sets show that OFS-A3M 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.