Review of swarm intelligence-based feature selection methods

• Published in: Engineering applications of artificial intelligence 2021-04, Vol.100, p.104210, Article 104210
• Main Authors: Rostami, Mehrdad, Berahmand, Kamal, Nasiri, Elahe, Forouzandeh, Saman
• Format: Article
• Language: English
• Subjects: Dimensionality reduction; Evolutionary algorithms; Feature selection; Machine learning; Swarm intelligence
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2021.104210

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