Wild Horse Optimizer-Based Spiral Updating for Feature Selection

Abstract

Feature selection (FS) is a vital and challenging process in several domains, including data mining, data clustering, text mining, education, biology, medicine, public health, machine learning, image processing, and so on. The greedy and comprehensive algorithm methods cannot identify the best subset amid the rising number of features. Thus, swarm-based algorithms are becoming more popular for identifying the best group of features. This study relies on the spiral-updated position of the Whale Optimization Algorithm (WOA) to propose an improved version of the Wild Horse Optimizer (WHO). This improvement enhances the WHO’s ability to update solutions and explore various possibilities in the search domain. The proposed method (WHOW) was assessed using two experiments to confirm the efficacy of the improved optimizer. The first experiment was global optimization using CEC 2019 benchmark functions, whereas the second was an FS by testing 20 benchmark datasets. The results obtained using the proposed WHOW method were compared to some popular algorithms over the benchmark datasets of global optimization and FS. The experimental results reflect the superiority of WHOW in the solutions to different optimization problems and its ability to select prominent features over most benchmark datasets. These results are due to the implementation of bubble nets in the WHO using spiral movement, which promotes flexibility and performance.

Keywords

• Wild horse optimizer
• whale optimization algorithm
• spiral updating position
• global optimization
• feature selection