An evolutionary feature selection method based on probability-based initialized particle swarm optimization

Feature selection is a common data preprocessing technique that aims to construct better models by selecting the most predictive features. Existing particle swarm optimization-based feature selection algorithms encounter two challenges when dealing with high-dimensional problems: easy to fall into local optimum and high computational cost. Therefore, this paper proposes an evolutionary dual-task feature selection method based on probability-based initialization particle swarm optimization (PPSO-EDT), which aims to find optimal solutions by transferring knowledge between two related tasks. Firstly, a probability-based initialization strategy is designed to accelerate population convergence by fully utilizing the correlation between labels and features. Secondly, a task generation strategy based on feature correlation was designed, which constructs the main task and auxiliary task by selecting feature subsets with highly correlated values and feature subsets without redundancy, respectively. Finally, an multi-task transfer mechanism is used to transfer knowledge and find optimal solutions. The results on 12 high-dimensional datasets indicate that the proposed method achieves high classification performance with a small feature subset in a relatively short amount of time.