A Hybrid Rough Set-Based Binary Grasshopper Optimization Algorithm for Feature Selection

Abstract

     Feature Selection (FS) is a technique that removes redundant and unnecessary characteristics from the original data to identify the smallest subset of features. Its goal is to make classification algorithms more efficient. Rough set theory (RST) offers a reliable route to feature selection; however, it resorts to comprehensive searches to find all subsets of features and dependence to assess them. However, due to its high cost, the entire search may not be viable for huge data sets. As a result, meta-heuristic algorithms, particularly Nature-Inspired Algorithms, are commonly employed to substitute the RST reduction step. The Hybrid Rough Set based Binary Grasshopper Optimization Algorithm (HRBGOA) was proposed as a FS approach for given datasets using BGOA with Rough Set to achieve significant Size Reduction Proportion (SR%) without significantly lowering classification accuracy compared to the total number of features in a smaller number of iterations. Compared to the Binary Grasshopper Optimization Algorithm (BGOA) and Particle Swarm Optimization (PSO) techniques, the experimental findings reveal that HRBGOA produced improved FS in seven datasets.