A Deep Reinforcement Learning Based Feature Selector

In the field of data mining and machine learning, it is a challenge for researchers and engineers to analyze and classify the high-dimensional data. In order to minimize the classification error, it is critical to identify and select the most characterizing features as well as remove the irrelevant features from the high-dimensional data. Feature selection algorithms provide effective processes to find a compact valuable feature subset from the set of all the candidate features. In past researches, most feature selection methods treat such a problem as a scoring problem, where each feature is evaluated individually and top-ranked features are selected. In this study, adding new features into the feature subset is considered as a Markov Decision Process, and the Feature Selection task is formalized as a reinforcement learning problem. This paper proposes a novel Deep Reinforcement Learning based Feature Selector (DRLFS) along with a dynamic randomness policy and two search protocols to tackle the exploration versus exploitation dilemma for a gradual approach to the optimum subset. The experimental results on various benchmark datasets prove the promising feature selection ability of the proposal of this paper.