A New Feature Selection Algorithm Based on Deep Q-Network
Xinqian Li, Jie Yao, Jia Ren, Liqiang Wang · 2021
In machine learning tasks, feature selection is an important data preprocessing step. It improves the efficiency and prediction accuracy by removing redundant and irrelevant features in a dataset. Feature selection is essentially a search process for the optimal feature subset. Although the traditional feature selection methods can search feature subset effectively, they still have the problems of large scale and low classification accuracy. In this paper, a new automatic feature selection method based on Deep Q-Network (DQN-FS) is proposed to solve the shortcomings of traditional feature selection. Firstly, the feature selection problem is formulated as a Markov decision process. Subsequently, the Q network is used to optimize the search strategy of the optimal feature subset. Finally, the parameters of the Q network are updated by stochastic gradient descent. This paper also considers an extensive study of the feature sequence setting for the proposed technique. The proposed methods are tested on 14 UCI standard datasets and then compared with mainstream feature selection methods. The results show the efficiency of DQN- FS in searching for the optimal feature subsets.