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

Abstract

Feature selection problem represents the field of study that requires approximate algorithms to identify discriminative and optimally combined features. The evaluation and suitability of these selected features are often analyzed using classifiers. These features are locked with data increasingly being generated from different sources such as social media, surveillance systems, network applications, and medical records. The high dimensionality of these datasets often impairs the quality of the optimal combination of these features selected. The use of the binary optimization method has been proposed in the literature to address this challenge. However, the underlying deficiency of the single binary optimizer is transferred to the quality of the features selected. Though hybrid methods have been proposed, most still suffer from the inherited design limitation of the single combined methods. To address this, we proposed a novel hybrid binary optimization capable of effectively selecting features from increasingly high-dimensional datasets. The approach used in this study designed a sub-population selective mechanism that dynamically assigns individuals to a 2-level optimization process. The level-1 method first mutates items in the population and then reassigns them to a level-2 optimizer. The selective mechanism determines what sub-population is assigned for the level-2 optimizer based on the exploration and exploitation phase of the level-1 optimizer. In addition, we designed nested transfer (NT) functions and investigated the influence of the function on the level-1 optimizer. The binary Ebola optimization search algorithm (BEOSA) is applied for the level-1 mutation, while the simulated annealing (SA) and firefly (FFA) algorithms are investigated for the level-2 optimizer. The outcome of these are the HBEOSA-SA and HBEOSA-FFA, which are then investigated on the NT, and their corresponding variants HBEOSA-SA-NT and HBEOSA-FFA-NT with no NT applied. The hybrid methods were experimentally tested over high-dimensional datasets to address the challenge of feature selection. A comparative analysis was done on the methods to obtain performance variability with the low-dimensional datasets. Results obtained for classification accuracy for large, medium, and small-scale datasets are 0.995 using HBEOSA-FFA, 0.967 using HBEOSA-FFA-NT, and 0.953 using HBEOSA-FFA, respectively. Fitness and cost values relative to large, medium, and small-scale datasets are 0.066 and 0.934 using HBEOSA-FFA, 0.068 and 0.932 using HBEOSA-FFA, with 0.222 and 0.970 using HBEOSA-SA-NT, respectively. Findings from the study indicate that the HBEOSA-SA, HBEOSA-FFA, HBEOSA-SA-NT and HBEOSA-FFA-NT outperformed the BEOSA.

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.…”

“…However, handling the problem of multimodality with respect to eliminating the bottlenecking effect of large features remains unaddressed. Although textual modality when served as input to machine learning classifiers have benefited from the use of binary optimization methods 32 in dimensionality reduction on features extracted. Research in the use of visual single modality such as the medical images, usually yields a staggering number of features as output from the convolutional-pooling layers of deep learning.…”

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

“…Oyelade et al 26 proposed a novel hybrid binary optimization approach for effective feature selection in high-dimensional datasets. Their approach included a subpopulation selective mechanism that dynamically assigned individuals to a 2-level optimization process.…”

Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications

“…The authors report the best and worst solutions obtained, the average of the different executions performed, and their standard deviation. This metric has been used in [21,31,33,46,48,50,53,54,57,60,[78][79][80][81][82]84,85,[87][88][89][90][91][92][93][94][95][96][97][98][99][101][102][103][105][106][107][108]110,112,114,115,[142][143][144][145][146][147][148][149][151]…”