I'm looking for an overview of some methods of variable selection.
I use datasets with around 6000 variables (the level of missing values is satisfying i.e. there are no variables with 100% missing values). There is no problem of insufficient degrees of freedom in my datasets.
In order to use some data mining techniques (such as regression, gradient boosting, etc) I use some methods of removing "weak" variables.
I am aware of stepwise methods for regression, but I'm looking for use with broader range of methods.
Does anyone know of any scientific papers or other articles about this subject?