A New Online Feature Selection Method Using Neighborhood Rough Set

Online feature selection, as a new method 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. In this paper, we define a new Neighborhood Rough Set relation with adapted neighbors and propose a new online streaming feature selection method based on this relation. Our approach 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, our new approach can select features with high correlation, high dependency and low redundancy. Experimental studies on ten different types of data sets show that our approach 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.