BGOA-TVG: A binary grasshopper optimization algorithm with time-varying Gaussian transfer functions for feature selection

Abstract

Abstract Feature selection aims to select crucial features to improve classification accuracy in machine learning and data mining. In this paper, a new binary grasshopper optimization algorithm using time-varying Gaussian transfer functions (BGOA-TVG) is proposed for feature selection. Compared with the traditional S-shaped and V-shaped transfer functions, the proposed Gaussian time-varying transfer functions has the characteristics of fast convergence speed and strong global search capability to convert the continuous search space to the binary one. The BGOA-TVG is tested and compared to S-shaped, V-shaped binary grasshopper optimization algorithm and five state-of-the-art swarm intelligence algorithms in feature selection. The experimental results show that BGOA-TVG has better performance in UCI and DEAP datasets for the feature selection.