A novel feature selection method based on adaptive search particle swarm optimization

As an effective method for data dimensionality reduction, feature selection could improve the classification accuracy and reduce the computational cost when dealing with high-dimensional data. Feature selection is essentially a complex optimization search problem. Among many optimization algorithms,...

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Bibliographic Details
Published in:	Neural computing & applications Vol. 37; no. 12; pp. 7767 - 7783
Main Authors:	Han, Fei, Wang, Yi-Huai, Li, Fan-Yu
Format:	Journal Article
Language:	English
Published:	London Springer London 01.04.2025 Springer Nature B.V
Subjects:	Adaptive search techniques Algorithms Artificial Intelligence Classification Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Correlation coefficients Data Mining and Knowledge Discovery Feature selection Image Processing and Computer Vision Minima Optimization Particle swarm optimization Probability and Statistics in Computer Science Redundancy S.I.: From Theory to Practice: Real-World Applications of AI in Data Science Search methods Special Issue on From Theory to Practice: Real-World Applications of AI in Data Science Adaptive search Feature selection Feature correlation Particle swarm optimization
ISSN:	0941-0643, 1433-3058
Online Access:	Get full text
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