Feature selection based on improved binary global harmony search for data classification

Harmony search (HS) is an effective meta-heuristic algorithm inspired by the music improvisation process, where musicians search for a pleasing harmony by adjusting their instruments’ pitches. The HS algorithm and its variants have been widely used to solve binary and continuous optimization problems. In this paper, we propose an improved binary global harmony search algorithm, called IBGHS, to undertake feature selection problems. A modified improvisation step is introduced to enhance the global search ability and increase the convergence speed of the algorithm. In addition, the K-nearest neighbor (KNN) is used as an underlying learning model to evaluate the effectiveness of the selected feature subsets. The experimental results on eighteen benchmark problems indicate that the proposed IBGHS algorithm is able to produce comparable results as compared with other state-of-the-art population-based methods such as genetic algorithm (GA), particle swarm optimization (PSO), antlion optimizer (ALO), novel global harmony search (NGHS) and whale optimization algorithm (WOA) in solving feature selection problems.