• Vijay Hasan Puri
• Jyoti Vashishtha

Classification problems occurred when unusual, irrelevant and redundant features are selected. This may degrade the classification performance and classification accuracy. Feature selection approach intended to select the less number of features which are relevant for improvement in classification performance than using all the features of corresponding dataset. Particle Swarm Optimization (PSO) approach is used for feature selection to achieve better results than traditional feature selection methods. Due to PSO’s simplicity, many researchers attracted towards PSO. This paper proposes a concise audit on PSO approaches for highlight determination. Advancement, enhancements and alterations on PSO algorithms for feature selection are examined in this paper.

Feature Selection, Multi-Objective Feature Selection, Particle Swarm Optimization, Multi-Objective PSO

"Feature Selection Using Particle Swarm Optimization : A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.618-623, June-2019, Available :http://www.jetir.org/papers/JETIR1907085.pdf

"Feature Selection Using Particle Swarm Optimization : A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp618-623, June-2019, Available at : http://www.jetir.org/papers/JETIR1907085.pdf