This Special Issue brings together cutting-edge research on algorithms, with a particular emphasis on feature selection techniques. Covering a broad range of topics-including evolutionary and ensemble methods, deep learning, high-dimensional data, time-series analysis, and textual applications-it addresses both theoretical advancements and real-world implementations. After undergoing a rigorous peer review process, ten high-quality papers were accepted for publication within this Special Issue. The research highlights include novel models for categorical feature independence, affordable housing analysis via scenario modeling, AI-driven educational engagement strategies, video content synchronization detection, fatigue detection in drivers using multimodal sensors, and advanced feature selection techniques for bioinformatics and cancer genomics. Further contributions demonstrate applications in author identification, time-series human motion analysis, and scheduling optimization through genetic programming. This Special Issue serves as a valuable reference for researchers aiming to explore the evolving landscape of feature selection in diverse, data-intensive domains.

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