Wild Horse Optimizer-Based Spiral Updating for Feature Selection

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