Newton’s second law based PSO for feature selection: Newtonian PSO

Abstract

High dimensional data have brobdingnagian number of features, but not all features are useful. Irrelevant and redundant features may even reduce the classification accuracy. Feature selection is a process of selecting a subset of relevant features to decrease the dimensionality of data. When applied on high dimensional datasets (Big Data) the feature selection methods perceives many challenges and it is pertinent to come up with the new methods or revamp the existing methods. In this study, a new method ‘Newtonian particle swarm optimization (NPSO)’ has been proposed. In the proposed method Newton’s second law of motion has been used to update the learning mechanism of PSO. In NPSO, particle not only learn from the position but also from the mass and acceleration of neighboring particles. The proposed method is mathematically validated at equilibrium using eigen values. Further, the proposed method has been applied on high dimensional microarray gene expression dataset. The NPSO is also compared with other state of art feature selection methods. Selected features, classification accuracy and dimension reduction are used to appraise the goodness of the proposed method. Mathematical validation and experimental results clearly validates the merits of the proposed method in field of feature selection. This paper show the classwise analysis of SRBCT, Brain1, 11-Tumor and 14-Tumor datasets. When number of classes increased dimension reduction is increased but classification accuracy of dataset is decreased.