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Diya Kapoor
Independent Researcher
India
Abstract
This manuscript investigates the application of genetic algorithms (GAs) for feature selection in pattern recognition tasks, focusing on techniques and technologies available up to 2014. Feature selection is crucial for improving classification performance, reducing computational cost, and enhancing generalization. We review GA-based wrapper, filter, and hybrid approaches, propose a methodological framework integrating GA with support vector machines (SVMs) for evaluating candidate feature subsets, and present experimental results on benchmark datasets. A statistical analysis table summarizes the performance gains over baseline methods. Research gaps and future directions are identified, highlighting the need for improved convergence speed and robustness to noisy data. The study demonstrates that GA-based feature selection can yield significant accuracy improvements, paving the way for more efficient pattern recognition systems.
Keywords
Genetic algorithms, feature selection, pattern recognition, wrapper methods, filter methods
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