  Feature selection, as a critical pre-processing step for machine learning,
aims at determining representative predictors from a high-dimensional feature
space dataset to improve the prediction accuracy. However, the increase in
feature space dimensionality, comparing to the number of observations, poses a
severe challenge to many existing feature selection methods with respect to
computational efficiency and prediction performance. This paper presents a new
hybrid two-layer feature selection approach that combines a wrapper and an
embedded method in constructing an appropriate subset of predictors. In the
first layer of the proposed method, the Genetic Algorithm(GA) has been adopted
as a wrapper to search for the optimal subset of predictors, which aims to
reduce the number of predictors and the prediction error. As one of the
meta-heuristic approaches, GA is selected due to its computational efficiency;
however, GAs do not guarantee the optimality. To address this issue, a second
layer is added to the proposed method to eliminate any remaining
redundant/irrelevant predictors to improve the prediction accuracy. Elastic
Net(EN) has been selected as the embedded method in the second layer because of
its flexibility in adjusting the penalty terms in regularization process and
time efficiency. This hybrid two-layer approach has been applied on a Maize
genetic dataset from NAM population, which consists of multiple subsets of
datasets with different ratio of the number of predictors to the number of
observations. The numerical results confirm the superiority of the proposed
model.
