An efficient ranking based binary salp swarm optimization for feature selection in high dimensional datasets

Abstract

Feature selection is a major challenge in data mining which involves complex searching procedure to acquire relevant feature subset. The effectiveness of classification approaches is greatly susceptible to data dimensionality. The Higher dimensionality intricate numerous problems like higher computational costs and over fitting problem. The essential key factor to mitigate the problem is feature selection. The main motive is to minimize the number of features through eliminating noisy, insignificant, and redundant features from the original data. The Metaheuristic algorithm attains excellent performance for solving this kind of problems. In this paper, the grading based binary salp swarm optimization has been proposed to solve various complex problems with lesser computational time. The grading system has been used to maintain the balance among exploitation and exploration. The proposed method is examined using ten benchmark real datasets. The comparative result exhibits the promising performance of our proposed method and surpasses with other optimization interms of investigating evaluation measures.