An efficient binary Gradient-based optimizer for feature selection

Abstract

Feature selection (FS) is a classic and challenging optimization task in the field of machine learning and data mining. Gradient-based optimizer (GBO) is a recently developed metaheuristic with population-based characteristics inspired by gradient-based Newton's method that uses two main operators: the gradient search rule (GSR), the local escape operator (LEO) and a set of vectors to explore the search space for solving continuous problems. This article presents a binary GBO (BGBO) algorithm and for feature selecting problems. The eight independent GBO variants are proposed, and eight transfer functions divided into two families of S-shaped and V-shaped are evaluated to map the search space to a discrete space of research. To verify the performance of the proposed binary GBO algorithm, 18 well-known UCI datasets and 10 high-dimensional datasets are tested and compared with other advanced FS methods. The experimental results show that among the proposed binary GBO algorithms has the best comprehensive performance and has better performance than other well known metaheuristic algorithms in terms of the performance measures.