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Screening feature selection methods are often used as a preprocessing stepfor reducing the number of variables before training step. Traditionalscreening methods only focus on dealing with complete high dimensionaldatasets. Modern datasets not only have higher dimension and larger samplesize, but also have properties such as streaming input, sparsity and conceptdrift. Therefore a considerable number of online feature selection methods wereintroduced to handle these kind of problems in recent years. Online screeningmethods are one of the categories of online feature selection methods. Themethods that we proposed in this research are capable of handling all threesituations mentioned above. Our research study focuses on classificationdatasets. Our experiments show proposed methods can generate the same featureimportance as their offline version with faster speed and less storageconsumption. Furthermore, the results show that online screening methods withintegrated model adaptation have a higher true feature detection rate thanwithout model adaptation on data streams with the concept drift property. Amongthe two large real datasets that potentially have the concept drift property,online screening methods with model adaptation show advantages in either savingcomputing time and space, reducing model complexity, or improving predictionaccuracy.

Online Feature Screening for Data Streams with Concept Drift