  Screening feature selection methods are often used as a preprocessing step
for reducing the number of variables before training step. Traditional
screening methods only focus on dealing with complete high dimensional
datasets. Modern datasets not only have higher dimension and larger sample
size, but also have properties such as streaming input, sparsity and concept
drift. Therefore a considerable number of online feature selection methods were
introduced to handle these kind of problems in recent years. Online screening
methods are one of the categories of online feature selection methods. The
methods that we proposed in this research are capable of handling all three
situations mentioned above. Our research study focuses on classification
datasets. Our experiments show proposed methods can generate the same feature
importance as their offline version with faster speed and less storage
consumption. Furthermore, the results show that online screening methods with
integrated model adaptation have a higher true feature detection rate than
without model adaptation on data streams with the concept drift property. Among
the two large real datasets that potentially have the concept drift property,
online screening methods with model adaptation show advantages in either saving
computing time and space, reducing model complexity, or improving prediction
accuracy.
