Review of swarm intelligence-based feature selection methods

In the past decades, rapid growth of computer and database technologies has led to large-scale datasets. On other hand, data mining applications with high dimensional datasets that require speed accuracy are rapidly increasing. An important issue these is curse dimensionality, where number features much higher than patterns. One dimensionality reduction approaches feature selection can increase task reduce its computational complexity. The method aims at selecting a subset lowest inner similarity highest relevancy target class. It reduces by eliminating irrelevant, redundant, or noisy data. this paper, comparative analysis different methods presented, general categorization performed. Moreover, in state-of-the-art swarm intelligence studied, recent based on algorithms reviewed. Furthermore, strengths weaknesses studied intelligence-based evaluated. • paper an overview problem provided. Previous categorized advantages disadvantages described. Swarm Intelligence-based Experimental results reported. previous discussed.