Binary Ebola Optimization Search Algorithm for Feature Selection and Classification Problems

Abstract

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.

A twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification

“…The binary optimizer is popular with use in the feature selection on binary classification problem. Binary Ebola optimization search algorithm (BEOSA) is one of recent state-of-the-art methods 32 , 71 derived from the continuous metaheuristic method namely Ebola optimization search algorithm (EOSA) 4 , 72 . In this subsection, a brief discussion on the optimization process of the BEOSA is presented, with emphasis on the use of this method to address the optimization of features extracted during the convolutional operations.…”

Evolutionary binary feature selection using adaptive ebola optimization search algorithm for high-dimensional datasets

“…The BEOSA [57] is a recent binary optimizer derived from the EOSA metaheuristics [58] and the immunity-based variant IEOSA [35]. The foundational design of the EOSA method was inspired by the Ebola virus and its associated propagation method.…”

“…Nevertheless, we consider that a novel optimization and transformation outcome can be achieved using a nested transfer function. As a result, we modeled eight different transfer functions taking a cue from the basic S and V functions applied in our recent study [57]. In that previous study, we proposed using the S1 and S2 for the S-family and the V1 and V2 for the V-family transfer function.…”

“…Recent technological advances have led to an increase in the amount of data generated and stored. The increase is in the volume and nature of the data, usually having large dimensions or features, outliers, skewness, missing values, redundant features, irrelevant data, integration, and heterogeneity [1,2]. This increase significantly reduces the classifier's accuracy, and the ability to manipulate this data decreases, too [3].…”

A novel feature selection algorithm for identifying hub genes in lung cancer

“…Akinola et al 27 introduced a novel feature selection model called binary Ebola optimization search algorithm (BEOSA). Their proposed model incorporated V-shape and S-shape transfer functions to guide the mutation process in the exploitation and exploration phases.…”