Correlation-Guided Updating Strategy for Feature Selection in Classification With Surrogate-Assisted Particle Swarm Optimization

Classification data are usually represented by many features, but not all of them are useful. Without domain knowledge, it is challenging to determine which features are useful. Feature selection is an effective preprocessing technique for enhancing the discriminating ability of data, but it is a difficult combinatorial optimization problem because of the challenges of the huge search space and complex interactions between features. Particle swarm optimization (PSO) has been successfully applied to feature selection due to its efficiency and easy implementation. However, most existing PSO-based feature selection methods still face the problem of falling into local optima. To solve this problem, this article proposes a novel PSO-based feature selection approach, which can continuously improve the quality of the population at each iteration. Specifically, a correlation-guided updating strategy based on the characteristic of data is developed, which can effectively use the information of the current population to generate more promising solutions. In addition, a particle selection strategy based on a surrogate technique is presented, which can efficiently select particles with better performance in both convergence and diversity to form a new population. Experimental comparing the proposed approach with a few state-of-the-art feature selection methods on 25 classification problems demonstrate that the proposed approach is able to select a smaller feature subset with higher classification accuracy in most cases.

Automated summary

This article introduces a novel PSO-based feature selection approach that continuously improves population quality and performance through correlation-guided updating and surrogate technique. Experimental results demonstrate its outstanding performance in classification accuracy.