Efficient feature selection method using real-valued grasshopper optimization algorithm.

Highlights • Grasshopper optimization algorithm is used to propose a feature selection method. • Computation of feature goodness factor helps to find global solution. • The accuracy of 100% is achieved by selecting 4 features out of 2308 in a dataset. • The method achieves the highest classification accuracy in 7 out of 10 datasets. Abstract Feature selection is the problem of finding the minimum number of features among a redundant feature space which leads to the maximum classification performance. In this paper, we have proposed a novel feature selection method based on mathematical model of interaction between grasshoppers in finding food sources. Some modifications were applied to the grasshopper optimization algorithm (GOA) to make it suitable for a feature selection problem. The method, abbreviated as GOFS is supplemented by statistical measures during iterations to replace the duplicate features with the most promising features. Several publicly available datasets with various dimensionalities, number of instances, and target classes were considered to evaluate the performance of the GOFS algorithm. The results of implementing twelve well-known and recent feature selection methods were presented and compared with GOFS algorithm. Comparative experiments indicate the significance of the proposed method in comparison with other feature selection methods. [ABSTRACT FROM AUTHOR]