Improved Shuffled Binary Grasshopper Optimization Feature Selection Algorithm

Abstract

Feature selection is to select the optimal or relatively optimal feature subsets from the original feature set of the data set to speed up classification and improve classification accuracy. An improved shuffled binary grass-hopper optimization feature selection algorithm is proposed in this paper. By introducing a binary transformation strategy that uses step size to guide individual position change, the blindness of the binary conversion is reduced, and the search performance of the algorithm in solution space is improved. By introducing shuffled complex evolution, the grasshopper population is divided into subgroups and evolved independently, which improves the diversity of algorithm and reduces the probability of premature convergence. The improved algorithm is used to select features of some data sets of UCI, and K-NN (K-nearest neighbor) classifier is used to classify and evaluate the feature subset. Experimental results show that compared with the basic binary grasshopper optimization algorithm, binary particle swarm optimization algorithm and binary gray wolf optimization algorithm, the improved algorithm has better search performance, convergence performance and strong robustness, and can obtain better feature subsets and better classification effect.

Keywords

• binary
• grasshopper optimization algorithm
• shuffled complex evolution
• feature selection
• classification
• k-nearest neighbor (k-nn) algorithm