Resumen
In the past decade, the extraction of valuable information from online biomedical datasets has exponentially increased due to the evolution of data processing devices and the utilization of machine learning capabilities to find useful information in these datasets. However, these datasets present a variety of features, dimensionalities, shapes, noise, and heterogeneity. As a result, deriving relevant information remains a problem, since multiple features bottleneck the classification process. Despite their adaptability, current state-of-the-art classifiers have failed to address the problem, giving rise to the exploration of binary optimization algorithms. This study proposes a novel approach to binarizing the Ebola optimization search algorithm. The binary Ebola search optimization algorithm (BEOSA) uses two newly formulated S-shape and V-shape transfer functions to investigate mutations of the infected population in the exploitation and exploration phases, respectively. A model is designed to show a representation of the binary search space and the mapping of the algorithm from the continuous space to the discrete space. Mathematical models are formulated to demonstrate the fitness and cost functions used for evaluating the algorithm. Using 22 benchmark datasets consisting of low, medium and high dimensional data, we exhaustively experimented with the proposed BEOSA method and six other recent similar feature selection methods. The experimental results show that the BEOSA and its variant BIEOSA were highly competitive with different state-of-the-art binary optimization algorithms. A comparative analysis of the classification accuracy obtained for eight binary optimizers showed that BEOSA performed competitively compared to other methods on nine datasets. Evaluation reports on all methods revealed that BEOSA was the top performer, obtaining the best values on eight datasets and eight fitness and cost functions. Computation for the average number of features selected showed that BEOSA outperformed other methods on 11 datasets when population sizes of 75 and 100 were used. Findings from the study revealed that BEOSA is effective in handling the challenge of feature selection in high-dimensional datasets.