Boruta
Boruta is a wrapper feature selection method in that it employs other feature selection methods to provide more accurate prediction results. Boruta compares the variables against randomized versions of themselves, and then ensures that only the features that perform better than the best randomized variable will be deemed as an important feature. The idea is that a model is only useful if it beats a randomized model which is not intended to have any correlation.