  With the decreasing cost of data collection, the space of variables or
features that can be used to characterize a particular predictor of interest
continues to grow exponentially. Therefore, identifying the most characterizing
features that minimizes the variance without jeopardizing the bias of our
models is critical to successfully training a machine learning model. In
addition, identifying such features is critical for interpretability,
prediction accuracy and optimal computation cost. While statistical methods
such as subset selection, shrinkage, dimensionality reduction have been applied
in selecting the best set of features, some other approaches in literature have
approached feature selection task as a search problem where each state in the
search space is a possible feature subset. In this paper, we solved the feature
selection problem using Reinforcement Learning. Formulating the state space as
a Markov Decision Process (MDP), we used Temporal Difference (TD) algorithm to
select the best subset of features. Each state was evaluated using a robust and
low cost classifier algorithm which could handle any non-linearities in the
dataset.
