A Fast Hybrid Feature Selection Based on Correlation-Guided Clustering and Particle Swarm Optimization for High-Dimensional Data

• Published in: IEEE transactions on cybernetics Vol. 52; no. 9; pp. 9573 - 9586
• Main Authors: Song, Xian-Fang, Zhang, Yong, Gong, Dun-Wei, Gao, Xiao-Zhi
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Clustering; Clustering algorithms; Computational efficiency; Computing costs; Convergence; Correlation; Evolutionary algorithms; Feature extraction; Feature selection; feature selection (FS); Genetic algorithms; hybrid search; Mutual information; Particle swarm optimization; particle swarm optimization (PSO); Search problems

The "curse of dimensionality" and the high computational cost have still limited the application of the evolutionary algorithm in high-dimensional feature selection (FS) problems. This article proposes a new three-phase hybrid FS algorithm based on correlation-guided clustering and particl...