A novel feature selection method based on adaptive search particle swarm optimization - Han, Fei; Wang, Yi-Huai; Li, Fan-Yu (Neural computing & applications, 2025)
As an effective method for data dimensionality reduction, feature selection could improve the classification accuracy and reduce the computational cost when dealing with high-dimensional data. Feature selection is essentially a complex optimization search problem. Among many optimization algorithms, particle swarm optimization (PSO) has been widely used due to its good global search ability and easy to implement. However, most feature selection methods based on PSO ignore the correlations between the features of data, which may lead to more redundant features, and the feature selection methods are prone to fall into local minima. This study proposes an improved feature selection method based on adaptive search particle swarm optimization (AS-BPSO-FS). On one hand, AS-BPSO-FS is designed to consider the feature correlation according to the feature correlation information. The particle position adaptive update strategy selects features based on the correlation coefficient between features, ensuring that features with higher correlation are more likely to be culled, so as to obtain a subset of features with less redundancy. On the other hand, AS-BPSO-FS identifies particles trapped in local minima by calculating the update time of individual and global optimal positions, and uses an adaptive particle neighborhood search strategy to help particles escape from local minima. The AS-BPSO-FS has been tested on 10 UCI data and compared with some state-of-the-art feature selection methods. The results verify that the proposed method could obtain feature subsets with better classification performance and less redundancy.