Integrating Data Augmentation in Evolutionary Algorithms for Feature Selection: A Preliminary Study

Tiziana D'Alessandro, Claudio De Stefano, Francesco Fontanella, Emanuele Nardone

Abstract: In many machine learning applications, there are hundreds or even thousands of features available, and selecting the smallest subset of relevant features is a challenging task. More recently, researchers have investigated how data augmentation affects feature selection performance. Although evolutionary algorithms have been widely used for feature selection, no studies have investigated how data augmentation affects their performance on this challenging task. The study presented in this paper investigates how data augmentation affects the performance of evolutionary algorithms on feature selection problems. To this aim, we have tested Genetic Algorithms and Particle Swarm Optimization and compared their performance with two widely used feature selection algorithms. The experimental results confirmed that data augmentation is a promising tool for improving the performance of evolutionary algorithms for feature selection.