Feature Selection using Grey Wolf Optimization Algorithm on Light Gradient Boosting Machine

Abstract

high dimensional data provide a major problem to supervised learning. In identifying high dimensional data, the learning models usually exhibit overfitting and become less understandable. One way to find the ideal features on high-dimensional data implemented feature selection on dataset Feature selection is one of the crucial aspects on data preprocessing step. Several algorithms for feature selection were proposed over the decades such as wrapper method, filter, and embedded method. In this research, we implemented wrapper method with Grey Wolf Optimization. We implemented Grey Wolf Optimization on wrapper method because the algorithm is efficient, simple and had lower computational time. We are also compared Grey Wolf Optimization to other meta-heuristic algorithms such as Particle Swarm Optimization and Genetic Algorithms. The result showed the GWO provide better computational time with the average time from four different dataset was 6.1125s. The accuracy result showed the GWO performed better on Ionosphere dataset.