• vikesh yadav
• dr. jyoti vashishtha

Abstract :-Feature selection is very important part of the classification process. It only selects that part of the data which is actually needed. The higher the complexity of features higher will be the complexity of classification. Doing an effective and efficient feature selection is very challenging process. Recently, the data dimensionality is increased, so many algorithms of feature selection confronts in terms of efficiency and effectiveness. Feature selection is done by using a classifier along with any optimization technique. There are many optimization techniques used in feature selection such as particle swarm optimization, genetic algorithm and ant colony optimization. Support vector machine, decision trees can be used as classifiers. The basic goal of this paper is to provide readers the overview of support vector machine and particle swarm optimization used for doing feature selection. This overview enables them to discover new SVM and PSO related strategies can be used to find best feature subset selection.

Keywords: feature selection, PSO, SVM

"A review on Feature Selection Technique based on Particle Swarm Optimization & SVM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.70-75, May-2019, Available :http://www.jetir.org/papers/JETIR1905013.pdf

"A review on Feature Selection Technique based on Particle Swarm Optimization & SVM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp70-75, May-2019, Available at : http://www.jetir.org/papers/JETIR1905013.pdf