A Review of Metaheuristic Optimization Techniques in Text Classification

Abstract

Metaheuristic algorithms, inspired by natural phenomena and human-based strategies, offer versatile approaches to navigate diverse search spaces and adapt to dynamic environments. These algorithms, including evolutionary algorithms, swarm intelligence, bio-inspired methods, human-based approaches, and plant-inspired techniques, have found applications across diverse domains such as engineering, finance, healthcare, logistics, and telecommunications. In the text classification domain, metaheuristic techniques have emerged as powerful tools to enhance the accuracy, efficiency, and robustness of classification systems. By optimizing feature subsets, fine-tuning model parameters, and addressing challenges such as feature selection, dimensionality reduction, class imbalance, and noisy data, metaheuristic algorithms provide flexible solutions that adapt to various text datasets and tasks. This review paper comprehensively explores recent advancements in metaheuristic applications in text classification across six categories. From evolutionary-based methods to swarm-based approaches, bio-inspired techniques to physics/chemistry-based strategies, human-based methods to plant-based algorithms, researchers have leveraged diverse metaheuristic techniques to push the boundaries of text classification. Through a systematic analysis of recent research studies, this review provides insights into the strengths, limitations, and future directions of metaheuristic optimization in the context of text classification.