  Feature selection has evolved to be an important step in several machine
learning paradigms. In domains like bio-informatics and text classification
which involve data of high dimensions, feature selection can help in
drastically reducing the feature space. In cases where it is difficult or
infeasible to obtain sufficient number of training examples, feature selection
helps overcome the curse of dimensionality which in turn helps improve
performance of the classification algorithm. The focus of our research here are
five embedded feature selection methods which use either the ridge regression,
or Lasso regression, or a combination of the two in the regularization part of
the optimization function. We evaluate five chosen methods on five large
dimensional datasets and compare them on the parameters of sparsity and
correlation in the datasets and their execution times.
