Feature selection optimization algorithm based on evolutionary Q-learning

Classification problems are an important research area in the field of data mining and machine learning. To enhance classification accuracy and optimize the effectiveness of learning algorithms, feature selection, as a data preprocessing operation, deserves ongoing attention. Based on reinforcement learning and particle swarm optimization, this paper proposes an evolutionary Q-learning feature selection optimization algorithm (EQL-FS). It leverages the advantages of reinforcement learning and combines them with the global exploration capability of the particle swarm optimization algorithm to achieve the optimal strategy. The multiagent approach is adopted, and the interaction is achieved through particle swarm optimization. The effectiveness of the proposed algorithm has been validated using sixteen public datasets. The experimental results indicate that this new algorithm can select the shortest feature subset without compromising accuracy. Additionally, it demonstrates a robust ability to eliminate noise and redundant features. Furthermore, the algorithm has been applied to analyze the broadband customer base churn for a communication operator, and the results are consistent with those obtained from the public datasets. Finally, the statistical test comparing different algorithms has been completed, and the results indicate that the new algorithm EQL-FS demonstrates statistical significance in terms of accuracy and the number of selected features.