Publication

Abstract : With the real time data, results in increasing in size. Feature selection (FS) has been considered as the problem of selecting these input features that are most predictive of a given outcome. Also current methods are inadequate. By considering this scenario, this paper proposes the incremental techniques; in fact this has found unsuccessful application in tasks that involve datasets contain huge number of features, which could be impossible to process further. For achieving this, these evolutionary techniques such as Genetic Algorithm, Particle Swarm Optimization Algorithm and Ant Colony Algorithm are considered for comparative performance analysis in which the experimental results shows that feature selection is best for minimal reductions.