Exploring Ant Colony Optimization for Feature Selection: A Comprehensive Review

Ant colony optimization (ACO) is a metaheuristic algorithm that handles complex optimization problems. ACO simulates the behavior of ants foraging in the real world to find the best solution. Several approaches have been presented to improve the performance of the ACO algorithm since its introduction. Feature selection is a preprocessing technique that selects the unique features with the most discriminative power to enhance classification accuracy in machine learning applications. The ACO algorithm has been used several times in feature selection tasks and has shown promising performance. In this chapter, we aim to review ACO-based feature selection methods. To this end, we will first introduce the ACO algorithm and then explain the issue of feature selection and its necessity in machine learning. Then, we will review the ACO-based feature selection methods presented thus far and classify these methods according to the presence of the class label (unsupervised, supervised, semisupervised) and interaction with the learning algorithm (filter, wrapper, embedded, and hybrid). Ultimately, we will discuss the challenges and issues that can still be solved using the ACO algorithm in feature selection.