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Swarm Intelligence Optimization for Feature Selection: Techniques, Applications, and Challenges for Enhanced Machine Learning Performance
Abstract
Feature selection is an important step in the preprocessing of data and attracts significant attention as an important preprocessing step for improving model performance and interpretability. With large datasets, straightforward approaches prove inefficient in dealing with high dimensions of the data. Inspired from the observation of the behaviors of natural systems in recent years, swarm intelligence has been proved to be an effective solution to optimization, for instance, selecting features. Algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Grey Wolf Optimizer (GWO) efficiently explore feature spaces, identifying optimal subsets that improve model accuracy while reducing computational overhead.This chapter also overviews the specific techniques of the swarm intelligence-based feature selection methods, along with the applications of their real-world performances in various fields, including healthcare, finance, and natural language processing.
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