Fitness and historical success information-assisted binary particle swarm optimization for feature selection

Abstract

Feature selection is a critical preprocessing step in machine learning aimed at identifying the most relevant features or variables from a dataset. Although conventional particle swarm optimization (PSO) has shown efficiency for feature selection tasks, developing an effective PSO algorithm for this task is still challenging. This study proposes a fitness and historical success information-assisted binary particle swarm optimization, denoted by FPSO. The FPSO is developed by integrating different search strategies, including a weighted center-based approach, historical information-based acceleration coefficients, and selection operation. These strategies are embedded into the FPSO to enhance the levels of exploration and exploitation based on the fitness value of particles and their historical search status. In the FPSO, the transfer function is also added to transform the continuous search space into binary search space. Experimental validation and comparison with seven other metaheuristic algorithms on 24 datasets verify the effectiveness of the FPSO in eliminating irrelevant and redundant features.