  The L1 regularization (Lasso) has proven to be a versatile tool to select
relevant features and estimate the model coefficients simultaneously and has
been widely used in many research areas such as genomes studies, finance, and
biomedical imaging. Despite its popularity, it is very challenging to guarantee
the feature selection consistency of Lasso especially when the dimension of the
data is huge. One way to improve the feature selection consistency is to select
an ideal tuning parameter. Traditional tuning criteria mainly focus on
minimizing the estimated prediction error or maximizing the posterior model
probability, such as cross-validation and BIC, which may either be
time-consuming or fail to control the false discovery rate (FDR) when the
number of features is extremely large. The other way is to introduce
pseudo-features to learn the importance of the original ones. Recently, the
Knockoff filter is proposed to control the FDR when performing feature
selection. However, its performance is sensitive to the choice of the expected
FDR threshold. Motivated by these ideas, we propose a new method using
pseudo-features to obtain an ideal tuning parameter. In particular, we present
the Efficient Tuning of Lasso (ET-Lasso) to separate active and inactive
features by adding permuted features as pseudo-features in linear models. The
pseudo-features are constructed to be inactive by nature, which can be used to
obtain a cutoff to select the tuning parameter that separates active and
inactive features. Experimental studies on both simulations and real-world data
applications are provided to show that ET-Lasso can effectively and efficiently
select active features under a wide range of scenarios

Machine Learning;Machine Learning