High Dimensional Feature Selection Method of Dual Gbest Based on PSO

Particle swarm optimization (PSO) has a bright future in feature selection (FS). However, with the increase of data set dimension, the search space becomes larger, PSO is easy to fall into local optima and brings a lot of time and space overhead. It is still a big challenge to apply PSO to thousands of feature data sets. In this paper, the features are reordered to ensure that the shorter particles can get better classification performance. We propose a subset length constraint mechanism to reduce the number of selected features of particles gradually, so as to introduce particles into smaller and more effective space and jump out of local optima. Although the short running time of filter FS is more suitable for high-dimensional feature selection, the classification accuracy of filter FS is generally lower than that of wrapper FS. In order to achieve better classification performance in a short time, we propose a dual global optimal (Gbest) updating model. PSO has been modified. Compared with six algorithms on six high-dimensional data sets. The results show that the new dual Gbest update mechanism based on length restriction mechanism has higher efficiency and accuracy.