An unsupervised feature selection algorithm based on ant colony optimization

By Sina Tabakhi, Parham Moradi, Fardin Akhlaghian Tab. Engineering applications of artificial intelligence, 2014

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Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification (2017, methodology): “Experimental results show that the proposed algorithm can automatically evolve a set of nondominated solutions, and it is a highly competitive feature selection method for solving cost-based feature selection problems.”
Unsupervised Feature Selection with Structured Graph Optimization (2016, methodology): “2007), wrapper method (Tabakhi, Moradi, and Akhlaghian 2014), and embedded method (Cai, Zhang, and He 2010; Zhao, Wang, and Liu 2010; Hou et al. 2014; Li et al. 2012; Wang, Tang, and Liu 2015).”